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Follow the steps to run a read-only pilot first, then decide which actions to automate.","\u003Csection data-gzh-readable=\"1\" style=\"margin:0;padding:0;\">\u003Cdiv data-gzh-header=\"1\">\u003Cp style=\"font-size:15px;color:#888;text-align:center;margin-top:8px;\">After reading, you will get a seven-category failure attribution table, three acceptance checklists, and autonomy thresholds. Follow the steps to run a read-only pilot first, then decide which actions should be executed automatically.\u003C\u002Fp>\n\u003Cfigure class=\"portal-article-figure\">\u003Cimg src=\"https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Freading_books.jpg\" alt=\"读书明理\" loading=\"lazy\" decoding=\"async\" \u002F>\u003Cfigcaption>读书明理\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\n\n\u003Cp style=\"font-size:14px;color:#999;text-align:center;margin-top:4px;\">Author: Jinxuezhai · Guanshan Academy\u003C\u002Fp>\u003Cp style=\"font-size:13px;color:#999;text-align:center;margin:0 0 6px 0;\">Full text: about 3,633 characters · estimated reading time: 13 minutes\u003C\u002Fp>\u003Chr style=\"border:none;border-top:1px solid #eee;margin:24px 0;\" \u002F>\u003C\u002Fdiv>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Recently, many teams have placed AI agents into real workflows: customer support tickets, operations inspections, data organization, internal approvals. The demo runs smoothly, but problems appear once multiple people, multiple systems, and long-running execution are involved. On the surface, performance seems unstable; the real blocker is that failure signals have not been broken down.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">This article provides an acceptance method: a seven-category failure postmortem table, three checklists, a three-day read-only pilot, and staged delegation. You can follow the steps to run a read-only pilot first, then decide which actions to automate.\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">I. Make Failure Explicit First: Seven Signals Determine Whether to Continue\u003C\u002Fh2>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">1. Task Understanding Drift: It Completes a Different Task\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">If the user asks the agent to organize customer complaints and generate reply drafts, it may only classify them. If the user asks it to fill in fields, it may rewrite the entire copy. The root cause of drift usually lies in goal decomposition, not the model itself.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">You can monitor first-pass success rate, human intervention rate, and the proportion of low-quality outputs. If the same intent frequently goes off track, first improve the task template and acceptance fields before considering prompts.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">2. Context Loss: Key Evidence Does Not Reach the Next Step\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">In multi-turn tasks, earlier constraints, customer tier, budget definitions, and historical handling conclusions may be dropped by the time tools are called. The result may look reasonable, but it cannot withstand review.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Observe context reference count, key-field accuracy, and task duration. If repeated queries and repeated confirmations increase, the pipeline is not passing evidence downstream.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">3. Tool Overreach: Actions That Should Not Be Taken Are Taken\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">A read API becomes a write API, a draft becomes a sent message, and a query becomes a deletion. This kind of issue is more dangerous than a wrong answer because it directly changes external state.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Record the number of sensitive-action hits, tool-call input parameters, caller identity, and target system. When overreach is found, first narrow the tool whitelist rather than adding a sentence saying “do not delete.”\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">4. Unverifiable Results: The Output Looks Good, but Cannot Be Traced\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The agent gives a conclusion but leaves no source of evidence, calculation definition, tool return value, or confidence indication. Business colleagues do not dare to sign off, and engineering colleagues cannot reproduce it.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Acceptance should examine key-field accuracy, completeness of the evidence chain, and whether users accept the result. Without verifiable fields, actions should not be handed to automated processes.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">5. Cost Runaway: One Task Turns into a Loop\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Repeated retrieval, repeated summarization, multiple model calls, and multiple tool requests cause the cost per task to keep rising. If the team only watches tokens, it can miss latency, retries, and manual review.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Track repeated call count, task duration, error recovery time, and per-task cost trend. Cost anomalies often mean the workflow has a loop; break the loop before scaling.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">6. Missing Approval: Automated Actions Bypass Humans\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">High-risk actions have no approval point, or approval is merely a formality. When something goes wrong, the chain of responsibility breaks, and the postmortem cannot recover why it was approved.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Check the missing-approval rate, number of external write actions, and retention period for approval records. Approval is not adding a button; it is binding each high-risk action to a person and a reason.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">7. Missing Rollback: Changes Cannot Be Reverted\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Editing documents, sending messages, updating tickets, and adjusting configurations—after failure, there is no rollback ID, no compensating action, and no fallback template. One success may conceal the next incident.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Record the rollback owner, error recovery time, idempotency, and audit ID. If write operations cannot be rolled back, allow them only in low-impact pilot scopes.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">\u003Cem>— First collect runtime logs, then determine whether the problem lies in the model, prompt, tools, or workflow.\u003C\u002Fem>\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Do not immediately switch frameworks, expand permissions, or add prompts. First make failures observable; only then does the team have a basis for discussion.\u003C\u002Fp>\u003Cp style=\"text-align:center;margin:16px 0 6px 0;\">\u003Cimg src=\"http:\u002F\u002Fmmbiz.qpic.cn\u002Fsz_mmbiz_jpg\u002F4WyfSFibfkROMdibe5hjUkfGygfhy7nUib2bicXAV0oicfMrpdCPHs1NaYib56OA6H166X6IVxU9xib3DlnNuy5icy5bxiaONJ9iaQ9yGpAoR7bgTjfJ0\u002F0?from=appmsg\" alt=\"Illustration · Lakeside Dusk\" style=\"max-width:100%;border-radius:6px;display:block;margin:0 auto;\" \u002F>\u003C\u002Fp>\u003Cp style=\"text-align:center;font-size:12px;color:#bbb;margin:0 0 18px 0;\">Illustration · Lakeside Dusk\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">II. Define Acceptance Criteria: Replace “Looks Usable” with Three Checklists\u003C\u002Fh2>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">1. Business Acceptance Checklist\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The business side asks only whether the result can be used. You can fix five items: task goal completion rate, key-field accuracy, whether users accept the result, proportion of low-quality outputs, and manual review ratio. Each item must specify its definition.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">For example, key customer-complaint fields include customer identity, request type, responsible team, and handling deadline. If one is missing, the task is not complete.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">2. Engineering Acceptance Checklist\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The engineering side must be able to reproduce, track, and stop losses. You can fix: timeout rate, retry count, idempotency, log-field completeness, dependency-service error codes, and call-chain latency distribution.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Logs must include at least \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">task_id\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">run_id\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">agent_version\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">tool_name\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">input_hash\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">output_hash\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">duration\u003C\u002Fcode>, and \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">error_code\u003C\u002Fcode>.\u003C\u002Fp>\u003Cpre style=\"background:#f5f7fa;padding:15px;border-radius:6px;font-size:14px;line-height:1.6;overflow-x:auto;margin:1em 0;\">\u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">event: agent.plan\u003Cbr>task_id: T-1024\u003Cbr>run_id: R-77\u003Cbr>user_intent: Organize customer complaints and generate reply drafts\u003Cbr>planned_steps: retrieve, summarize, draft\u003Cbr>tool_call: search_tickets status=open\u003Cbr>risk_level: read_only\u003Cbr>approval_required: false\u003Cbr>idempotency_key: T-1024-R-77-search-001\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">This log is not for appearance; it is for attribution after the three-day pilot. If there are only results and no process, the postmortem becomes guesswork.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">3. Security and Compliance Checklist\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The security focus is not model capability, but action boundaries. You need to check: exposure of sensitive data, external write actions, approvals for deletion\u002Fpayment\u002Fpublishing operations, retention period for audit records, and rollback owner.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Customer-facing outbound messages, fund changes, production configuration changes, and record deletion should not be automatically executed by default. Even if the business is pressing, run them first in a sandbox or shadow system.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Actionable Checklist\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">☐ Establish a three-key log of \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">task_id\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">run_id\u003C\u002Fcode>, and \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">agent_version\u003C\u002Fcode> to ensure every execution is traceable.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">☐ Record an input summary, output summary, duration, error code, and idempotency key for each tool call.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">☐ Place external writes, deletions, payments, publishing, and customer-facing outbound actions on a default block list.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">☐ Write clear definitions, criteria, and reviewers for business acceptance fields; do not accept vague evaluations.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">☐ Set up daily … for failure samples.\u003C\u002Fp>\u003C\u002Fsection>\n\u003Csection class=\"portal-disclaimer\" data-portal-disclaimer=\"1\" style=\"margin-top:2.5em;padding-top:1.25em;border-top:1px solid #e8e4dc;font-size:14px;line-height:1.7;color:#7a756c;\">\u003Cp style=\"margin:0;\">Disclaimer: This article is compiled from publicly available online sources for educational purposes only and does not represent the official position of Guanshan Academy. If you believe your rights have been infringed, please contact us for removal.\u003C\u002Fp>\u003Cp style=\"margin:0.75em 0 0;\">Contact: \u003Ca href=\"mailto:chenxj.g@gmail.com\">chenxj.g@gmail.com\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fsection>","","https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Freading_books.jpg","进学斋","ai","published",3491491,"2026-09-29T20:53:49+08:00","2026-09-29T21:12:09.724901+08:00","2026-09-29T21:35:02.881325+08:00",{"id":29,"locale":13,"slug":30,"type":15,"title":31,"summary":32,"content_html":33,"video_url":19,"cover_url":20,"author":21,"category":22,"status":23,"source_job_id":34,"published_at":35,"created_at":36,"updated_at":37},4590,"agent观测回滚-落地实战清单","Agent Observability and Rollback: A Practical Implementation Checklist","After reading, you will get a checklist of runtime observability fields for agents and an error-tier rollback table: start with low-traffic logging, then gradually grant more authority.","\u003Csection data-gzh-readable=\"1\" style=\"margin:0;padding:0;\">\u003Cdiv data-gzh-header=\"1\">\u003Cp style=\"font-size:15px;color:#888;text-align:center;margin-top:8px;\">After reading, you will get a checklist of runtime observability fields for agents and an error-tier rollback table: start with low-traffic logging, then gradually grant more authority.\u003C\u002Fp>\n\u003Cfigure class=\"portal-article-figure\">\u003Cimg src=\"https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Freading_books.jpg\" alt=\"读书明理\" loading=\"lazy\" decoding=\"async\" \u002F>\u003Cfigcaption>读书明理\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\n\n\n\n\n\u003Cp style=\"font-size:14px;color:#999;text-align:center;margin-top:4px;\">Author: Jinxuezhai · Guanshan Academy\u003C\u002Fp>\u003Cp style=\"font-size:13px;color:#999;text-align:center;margin:0 0 6px 0;\">Full text: about 3,580 characters · Reading time: about 12 minutes\u003C\u002Fp>\u003Chr style=\"border:none;border-top:1px solid #eee;margin:24px 0;\" \u002F>\u003C\u002Fdiv>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Several recent agent projects have entered the runtime phase, and the problems are becoming more complex: the model's answers look good, but the workflow is unstable. Issues range from missing prompts to excessive permissions, high costs, and unnoticed errors.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">This article focuses only on post-launch runtime data governance. You will get four observability dashboards, three levels of rollback actions, and a seven-day implementation plan. Start by recording low-volume traffic, then gradually grant more authority.\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">1. Implementation Bottlenecks: From 'The Model Can Answer' to 'The System Is Manageable'\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">After connecting agents to tools, many teams assume that 'being able to call a tool' means 'being ready for production.' In reality, failures rarely remain in the answer text; they more often appear in broken tool-call chains, permission overreach, uncontrolled costs, and unmanaged human takeover.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">For example, a query tool returns an empty value, and the agent continues to generate a conclusion; a write-back API times out, causing a task to be executed repeatedly; a sensitive operation is performed without approval and directly enters a production account. These problems cannot be fundamentally solved by ad hoc prompt edits.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">What must be done during runtime is to turn every execution into a traceable object. First, define stable execution: tasks must be traceable, errors must be stoppable, and costs must be rollback-capable.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Traceable: Every Task Must Have an Identity\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Without \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">task_id\u003C\u002Fcode> and \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">trace_id\u003C\u002Fcode>, agent calls are like anonymous requests. You do not know which role, workflow, or user batch they belong to.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Each call should record at least: task identity, scene, input digest, prompt version, model version, start time, and status.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Stoppable: Errors Must Be Tiered\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Not all errors should be retried. Parameter validation failures can prompt the user to make corrections; repeated tool failures should trigger circuit breaking; operations without approved permissions should stop immediately.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Stop conditions should be written into the system, not left to operations staff watching dashboards.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Rollbackable: Actions Need Exit Paths\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">If an agent only generates text, rollback is simple. Once it writes to a database, sends messages, or modifies tickets, you must know where the rollback point is.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Being rollback-capable does not mean undoing everything every time; it means ensuring that errors do not spread.\u003C\u002Fp>\u003Cp align=\"center\" style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">\u003Cem>— Only when it is visible can it be managed.\u003C\u002Fem>\u003C\u002Fp>\u003Cp style=\"text-align:center;margin:16px 0 6px 0;\">\u003Cimg src=\"http:\u002F\u002Fmmbiz.qpic.cn\u002Fmmbiz_jpg\u002F4WyfSFibfkRNicSJdZqVvCiasq28Q9qpTaIzccpTIdMFMV6CG1VdagjPZLT84B1Yicz5ppSlAZAShzH8OIJJ570JSEorQEyRGSLus3EPRJJYnLM\u002F0?from=appmsg\" alt=\"配图·山谷柔绿\" style=\"max-width:100%;border-radius:6px;display:block;margin:0 auto;\" \u002F>\u003C\u002Fp>\u003Cp style=\"text-align:center;font-size:12px;color:#bbb;margin:0 0 18px 0;\">Illustration · Valley in Soft Green\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">2. Observability Model: Four Data Dashboards\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Do not pile all logs into a single table. During agent runtime, you should split monitoring into at least four dashboards: requests, tools, cost and quality, and audit. Each dashboard answers a different question.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Request Dashboard: Whether a Task Was Triggered Correctly\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">It records who the request came from, what task it belongs to, and why it was triggered. It is useful for investigating why this agent started running.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">You can start by collecting a set of fields: \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">task_id\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">scene\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">input_digest\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">prompt_version\u003C\u002Fcode>, and \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">status\u003C\u002Fcode>. The number of fields matters less than being able to link them together.\u003C\u002Fp>\u003Cpre style=\"background:#f5f7fa;padding:15px;border-radius:6px;font-size:14px;line-height:1.6;overflow-x:auto;margin:1em 0;\">\u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">trace_id=agent-ticket-001\ntask_id=ticket-8827\nscene=ticket_first_reply\ninput_digest=order_delay_customer_question\nprompt_version=ticket_reply_v3\nstart_time=2026-09-29T10:20:00\nstatus=success\u003C\u002Fcode>\u003C\u002Fpre>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Tool Dashboard: Where the Chain Breaks\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The tool dashboard is more important than the answer dashboard. It records call order, return codes, latency, and failure points.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">It is best to break it down by step: step 1 queries the order, step 2 reads the knowledge base, step 3 generates a draft, and step 4 writes back to the ticket. If step 3 has no write-back, the problem is in the tool layer, not the model layer.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Fields: \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">step_no\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">tool_name\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">args_digest\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">return_code\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">latency_ms\u003C\u002Fcode>, and \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">retry_count\u003C\u002Fcode>.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Cost and Quality Dashboard: Whether It Is Worth Continuing to Grant Authority\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Agents are not better simply because they are more automated; you need to calculate the costs. Token usage, tool API fees, manual takeover rate, and repeat-question rate all determine whether traffic can be expanded.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">For quality metrics, do not look only at accuracy. You can also examine first-time completion rate, number of manual corrections, number of repeated triggers, and whether the result ultimately enters the business system.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Fields: \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">tokens_in\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">tokens_out\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">tool_cost_estimate\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">manual_takeover_reason\u003C\u002Fcode>, and \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">repeat_question_count\u003C\u002Fcode>.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Audit Dashboard: Who Approved Which Action\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">When actions involve ticket closure, customer notifications, refund requests, or inventory adjustments, auditing must be a separate dashboard.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">It records sensitive operations, approvers, execution results, and whether rollback occurred. Audit is not for the model; it is for the process.\u003C\u002Fp>\u003Cp align=\"center\" style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">\u003Cem>— Rollback is not a step backward; it preserves the right to continue running.\u003C\u002Fem>\u003C\u002Fp>\u003Cp style=\"text-align:center;margin:16px 0 6px 0;\">\u003Cimg src=\"http:\u002F\u002Fmmbiz.qpic.cn\u002Fsz_mmbiz_jpg\u002F4WyfSFibfkRMBOBM1DicBljibhC3e83iaaT74NwgoO06223ibzZsRbHtgsXfCgp4pEpRIZX9A2WOh9PY4pW622DbAYVMfFWvjXzznsnYqkMvPwGA\u002F0?from=appmsg\" alt=\"配图·静湖云烟\" style=\"max-width:100%;border-radius:6px;display:block;margin:0 auto;\" \u002F>\u003C\u002Fp>\u003Cp style=\"text-align:center;font-size:12px;color:#bbb;margin:0 0 18px 0;\">Illustration · Still Lake and Misty Clouds\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">3. Rollback Mechanism: Three Action Levels\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Many teams rely on gut feeling for rollback, which is dangerous. Real production deployment requires three levels of action: read-only observation, shadow execution, and minimal closed loop. Each level has permission boundaries and trigger conditions.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Read-Only Observation: Record First, Do Not Touch the Business\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Read-only observation is suitable for the first week of a new scenario. The agent can plan, query, and generate drafts, but it cannot write to production databases or send external messages.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The goal is not to replace humans, but to obtain evidence of the execution chain. You can...\u003C\u002Fp>\u003C\u002Fsection>\n\u003Csection class=\"portal-disclaimer\" data-portal-disclaimer=\"1\" style=\"margin-top:2.5em;padding-top:1.25em;border-top:1px solid #e8e4dc;font-size:14px;line-height:1.7;color:#7a756c;\">\u003Cp style=\"margin:0;\">Disclaimer: This article is compiled from publicly available online sources for educational purposes only and does not represent the official position of Guanshan Academy. If you believe your rights have been infringed, please contact us for removal.\u003C\u002Fp>\u003Cp style=\"margin:0.75em 0 0;\">Contact: \u003Ca href=\"mailto:chenxj.g@gmail.com\">chenxj.g@gmail.com\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fsection>",3491490,"2026-09-29T20:49:00+08:00","2026-09-29T21:00:41.908764+08:00","2026-09-29T21:35:01.807382+08:00",{"id":39,"locale":13,"slug":40,"type":15,"title":41,"summary":42,"content_html":43,"video_url":19,"cover_url":20,"author":21,"category":22,"status":23,"source_job_id":44,"published_at":45,"created_at":46,"updated_at":47},186,"agent落地-运行闭环七步法","AI Agent Deployment: The Seven-Step Operational Closed Loop","After reading, you will be able to build a post-launch closed loop for AI agent monitoring, alerting, human takeover, rollback, and evaluation, and use a two-week pilot to decide whether to continue, degrade, or stop.","\u003Csection data-gzh-readable=\"1\" style=\"margin:0;padding:0;\">\u003Cdiv data-gzh-header=\"1\">\u003Cp style=\"font-size:15px;color:#888;text-align:center;margin-top:8px;\">After reading, you will be able to build a post-launch closed loop for AI agent monitoring, alerting, human takeover, rollback, and evaluation, and use a two-week pilot to decide whether to continue, degrade, or stop.\u003C\u002Fp>\n\u003Cfigure class=\"portal-article-figure\">\u003Cimg src=\"https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Freading_books.jpg\" alt=\"读书明理\" loading=\"lazy\" decoding=\"async\" \u002F>\u003Cfigcaption>读书明理\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u003Cp style=\"font-size:14px;color:#999;text-align:center;margin-top:4px;\">Author: Jinxuezhai · Guanshan Academy\u003C\u002Fp>\u003Cp style=\"font-size:13px;color:#999;text-align:center;margin:0 0 6px 0;\">Full text about 3,943 characters · Estimated reading time 14 minutes\u003C\u002Fp>\u003Chr style=\"border:none;border-top:1px solid #eee;margin:24px 0;\" \u002F>\u003C\u002Fdiv>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Recently, while reviewing several teams that deliver AI agent implementations, I found that their on-site demos were mostly impressive: they could summarize, query databases, and fill in fields. But once connected to real requests, problems appeared: tool timeouts, mistaken permission denials, output drift, and no clear owner for human takeover. This is not because the prompts are not flashy enough; it is because the operational closed loop has not been established.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">This article focuses only on the post-launch operational closed loop: how to define normal, degraded, and circuit-breaking states; how to replay failures; what minimum viable monitoring looks like; how to prepare human takeover, regression evaluation, and rollback packages; and finally how to use a two-week pilot to decide whether to continue, degrade, or stop.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">According to industry observations, most AI agent incidents are not caused by a single model error, but by operational boundaries that were not clearly defined in advance. Only when the closed loop is built can teams rely less on ad-hoc firefighting.\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">1. First Define an Operational State Table: Normal, Degraded, and Circuit-Broken\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Many teams reduce AI agent states to success or failure. In production, failures often expand gradually. A parsing error may first cause missing fields, then trigger tool retries, and finally drive up costs. The meaning of a state table is to let the system know when to continue, when to reduce privileges, and when to stop.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">The State Table Should Answer Four Things\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">It must define entry conditions, system actions, recovery conditions, and responsible owners. Without these four items, on-duty personnel can only rely on intuition. The table below can be directly adapted into your team’s version.\u003C\u002Fp>\u003Ctable style=\"width:100%;border-collapse:collapse;font-size:13px;margin:16px 0;\">\u003Ctr>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">State\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Entry Condition\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">System Action\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Recovery Condition\u003C\u002Fth>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Normal\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Core tools are available, output validation passes, costs are within baseline, and sensitive operations are authorized.\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Low-risk tasks are executed automatically; medium-risk tasks generate suggestions; high-risk tasks are routed for approval.\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Evaluation and monitoring continue to pass, and the agent enters the next review cycle.\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Degraded\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Parsing failures, tool errors, or user corrections rise continuously; quality is unstable for a certain type of task; or costs grow abnormally.\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">High-risk automatic execution is disabled, while read-only queries, draft suggestions, and human confirmation are retained.\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">The failure rate falls within the continuous observation window, regression tests pass, and the responsible person approves restoration.\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Circuit-Broken\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Permission boundaries are crossed, critical dependencies are unavailable, output causes business impact, or cost or data risk becomes uncontrollable.\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">The entry point is disabled, evidence is preserved, the process is switched back to the previous workflow, and the responsible person and affected users are notified.\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">The root cause has been fixed, the incident retrospective is complete, and a small-scale pilot passes.\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftable>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">There is a practical principle here: degradation is not turning the AI agent into a decoration, but narrowing its capabilities to a range that is explainable, auditable, and recoverable.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">First Clarify Permissions for Three Task Types\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">If permission boundaries are not clearly written before launch, problems will appear during operation: the model suggests refunds, customer service directly modifies orders, or external actions are submitted without user confirmation. At minimum, divide tasks into three types:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong style=\"font-weight:bold;color:#1a1a1a;\">Can be executed automatically\u003C\u002Fstrong>: knowledge base retrieval, material summarization, initial ticket classification, field-extraction drafts, and read-only status queries. Outputs must be verifiable and must not directly change external state.\u003C\u002Fli>\u003Cli>\u003Cstrong style=\"font-weight:bold;color:#1a1a1a;\">Suggestion only\u003C\u002Fstrong>: contract clause modifications, fee explanations, risk scripts, and cross-department process recommendations. Progress is allowed only after human confirmation.\u003C\u002Fli>\u003Cli>\u003Cstrong style=\"font-weight:bold;color:#1a1a1a;\">Approval required\u003C\u002Fstrong>: refunds, account permission changes, data deletion, external sending, financial operations, and write actions that affect user rights. The approver, reason, and evidence must be retained.\u003C\u002Fli>\u003C\u002Ful>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Key Outputs Must Include Evidence Fields\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Without evidence fields, the group chat will only leave a sentence saying “something went wrong again.” Evidence should help people quickly answer: where the input came from, what the tool returned, whether validation passed, and which layer failed.\u003C\u002Fp>\u003Cpre style=\"background:#f5f7fa;padding:15px;border-radius:6px;font-size:14px;line-height:1.6;overflow-x:auto;margin:1em 0;\">\u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">input_source: ticket_id, doc_id, user_context_version\ntool_calls: retrieval_status, extraction_status, validation_status\noutput_check: schema_passed, safety_passed, business_rule_passed\nfailure_reason: plan_timeout, tool_error, permission_denied, user_correction\n\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">These fields do not necessarily all need to be shown to users, but they must be present in the logs.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">\u003Cem>— The state table is the control plane; evidence logs are the microscope.\u003C\u002Fem>\u003C\u002Fp>\u003Cp style=\"text-align:center;margin:16px 0 6px 0;\">\u003Cimg src=\"http:\u002F\u002Fmmbiz.qpic.cn\u002Fmmbiz_jpg\u002F4WyfSFibfkROdDNdbMQuptW8ogdgFPnJdzGnbEwAEXKeb6NmUrRcxgqkCV0g9eTkHU57XJbrodBxjX5DDzOvFeOvS5qxic8vAXcL9hTibLu7qk\u002F0?from=appmsg\" alt=\"Illustration · Warm Autumn Hues\" style=\"max-width:100%;border-radius:6px;display:block;margin:0 auto;\" \u002F>\u003C\u002Fp>\u003Cp style=\"text-align:center;font-size:12px;color:#bbb;margin:0 0 18px 0;\">Illustration · Warm Autumn Hues\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">2. Minimum Monitoring and Evidence Checklist\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Do not start monitoring by building a large dashboard. First capture five failure types; they are more actionable than overall accuracy.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Start with Five Failure Types\u003C\u002Fh3>\u003Cul>\u003Cli>\u003Cstrong style=\"font-weight:bold;color:#1a1a1a;\">Parsing failure\u003C\u002Fstrong>: fields are not extracted, JSON does not match the schema, or table rows and columns are misaligned. The problem is often in input format and schema constraints.\u003C\u002Fli>\u003Cli>\u003Cstrong style=\"font-weight:bold;color:#1a1a1a;\">Planning timeout\u003C\u002Fstrong>: task decomposition is too long, multi-round retrieval jumps repeatedly, or tool selection is hesitant. The problem is often in task boundaries and loop control.\u003C\u002Fli>\u003Cli>\u003Cstrong style=\"font-weight:bold;color:#1a1a1a;\">Tool error\u003C\u002Fstrong>: a dependent API returns an exception, permissions expire, or parameter mapping is wrong. The problem is often in the tool chain and error propagation.\u003C\u002Fli>\u003Cli>\u003Cstrong style=\"font-weight:bold;color:#1a1a1a;\">Permission denial\u003C\u002Fstrong>: the AI agent calls an action it should not call, or accesses unauthorized data. The problem is often in policy configuration and the approval chain.\u003C\u002Fli>\u003Cli>\u003Cstrong style=\"font-weight:bold;color:#1a1a1a;\">User correction\u003C\u002Fstrong>: the user explicitly says the answer is irrelevant, the evidence is insufficient, or the suggestion cannot be executed. The problem is often in task value and matching real scenarios.\u003C\u002Fli>\u003C\u002Ful>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Build Task-Level Logs\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The goal of logs is replayability. Given a request_id, you should be able to reconstruct which tools were called, which version was used, where it got stuck, and whether human takeover occurred. Fields do not need to be exhaustive, but they need to be stable.\u003C\u002Fp>\u003Cpre style=\"background:#f5f7fa;padding:15px;border-radius:6px;font-size:14px;line-height:1.6;overflow-x:auto;margin:1em 0;\">\u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">request_id\ntask_type\nagent_version\nprompt_version\ntool_call_chain\nlatency\ncost\nvalidation_status\nfail_layer\nhuman_takeover_count\nuser_feedback_id\n\u003C\u002Fcode>\u003C\u002Fpre>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Set Actionable Thresholds\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Thresholds do not need complex models; first cover changes that can cause incidents. You can use fixed windows or sliding windows, but every threshold must correspond to an action.\u003C\u002Fp>\u003Col>\u003Cli>If the same task fails consecutively up to a preset count, automatically switch to suggestion mode and pause write operations.\u003C\u002Fli>\u003Cli>If denials of sensitive tools surge, freeze related actions and check permission mapping and input sources.\u003C\u002Fli>\u003Cli>If costs rise abnormally compared with the historical baseline, enable caching, rate limiting, and short-answer mode.\u003C\u002Fli>\u003Cli>If human takeover and user corrections rise continuously, trigger regression evaluation and rollback assessment.\u003C\u002Fli>\u003C\u002Fol>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Minimum Monitoring and Evidence Checklist\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">☐ Each request has a unique request_id;\u003Cbr>☐ Each tool call has status, latency, and error code;\u003Cbr>☐ Each failure can be attributed to one of five layers: input, planning, tool, output, and permission;\u003Cbr>☐ Each degradation switch has a responsible owner;\u003Cbr>☐ Each human takeover has an approval record and evidence package;\u003Cbr>☐ Each version change has an old entry point and rollback path;\u003Cbr>☐ Each alert has a clear action, rather than only sending a notification.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">\u003Cem>— The meaning of a closed loop is to ensure that the next anomaly no longer relies on ad-hoc firefighting.\u003C\u002Fem>\u003C\u002Fp>\u003Cp style=\"text-align:center;margin:16px 0 6px 0;\">\u003Cimg src=\"http:\u002F\u002Fmmbiz.qpic.cn\u002Fsz_mmbiz_jpg\u002F4WyfSFibfkRP5WeeqJZQxz7mAH0BoQwyJMKcKP4sOtfgAIljZh9YTMqB0bVF1SKgREkNLmfricnn6MdyYpIeW1g6YRWhZOoflWgeuOuq5lXko\u002F0?from=appmsg\" alt=\"Illustration · Bamboo Shadows by the River\" style=\"max-width:100%;border-radius:6px;display:block;margin:0 auto;\" \u002F>\u003C\u002Fp>\u003Cp style=\"text-align:center;font-size:12px;color:#bbb;margin:0 0 18px 0;\">Illustration · Bamboo Shadows by the River\u003C\u002Fp>\u003C\u002Fsection>\n\u003Csection class=\"portal-disclaimer\" data-portal-disclaimer=\"1\" style=\"margin-top:2.5em;padding-top:1.25em;border-top:1px solid #e8e4dc;font-size:14px;line-height:1.7;color:#7a756c;\">\u003Cp style=\"margin:0;\">Disclaimer: This article is compiled from publicly available online sources for educational purposes only and does not represent the official position of Guanshan Academy. If you believe your rights have been infringed, please contact us for removal.\u003C\u002Fp>\u003Cp style=\"margin:0.75em 0 0;\">Contact: \u003Ca href=\"mailto:chenxj.g@gmail.com\">chenxj.g@gmail.com\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fsection>",3491459,"2026-09-29T17:56:37+08:00","2026-09-29T18:08:27.848109+08:00","2026-09-29T21:35:02.049844+08:00",{"id":49,"locale":13,"slug":50,"type":15,"title":51,"summary":52,"content_html":53,"video_url":19,"cover_url":54,"author":21,"category":22,"status":23,"source_job_id":55,"published_at":56,"created_at":57,"updated_at":58},188,"ai-agent落地实战-三流地图法","AI Agent Deployment in Practice: The Three-Flow Map Method","This article provides three diagrams—task flow, permission flow, and evidence flow—and a launch checklist to help developers determine whether an Agent can stably enter production and roll back safely.","\u003Csection data-gzh-readable=\"1\" style=\"margin:0;padding:0;\">\u003Cdiv data-gzh-header=\"1\">\u003Cp style=\"font-size:15px;color:#888;text-align:center;margin-top:8px;\">This article provides three diagrams—task flow, permission flow, and evidence flow—along with a launch checklist, helping developers determine whether an Agent can stably enter production and roll back safely.\u003C\u002Fp>\n\u003Cfigure class=\"portal-article-figure\">\u003Cimg src=\"https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Flandscape_mountain.jpg\" alt=\"观山静思\" loading=\"lazy\" decoding=\"async\" \u002F>\u003Cfigcaption>观山静思\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u003Cp style=\"font-size:14px;color:#999;text-align:center;margin-top:4px;\">Author: Jinxuezhai · Guanshan Academy\u003C\u002Fp>\u003Cp style=\"font-size:13px;color:#999;text-align:center;margin:0 0 6px 0;\">Full text approximately 3,523 characters · Reading time about 12 minutes\u003C\u002Fp>\u003Chr style=\"border:none;border-top:1px solid #eee;margin:24px 0;\" \u002F>\u003C\u002Fdiv>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Recently, I watched several teams bring AI Agents into real business workflows. The demos went smoothly: hand in a piece of material, and the model can summarize, fill in fields, and even offer suggestions. But once connected to ticketing systems, knowledge bases, approvals, and databases, problems quickly become slower, messier, and harder to trace. The real difficulty of deployment is not whether the model can talk, but whether it can be observed, constrained, and rolled back.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">This article does not discuss model selection or pile up prompt techniques. You will get a three-flow map: task flow, permission flow, and evidence flow, plus a launch checklist. After reading, you can clarify your scenario today, set up minimal logging tomorrow, and run a read-only trial the day after.\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">I. Don’t Start with the Model—Start with an Observable Task Flow\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Many failures are wrong from the very definition. The business side says “automatic processing,” while developers interpret it as letting the Agent improvise. This is not automation; it is risk expansion. First, rewrite the goal into four observable items.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">1. Input, Constraints, Output, and Exceptions\u003C\u002Fh3>\u003Cul>\u003Cli>\u003Cstrong style=\"font-weight:bold;color:#1a1a1a;\">Input\u003C\u002Fstrong>: What materials the Agent can access. For example: ticket text, user history, attachment links, current time, and customer identity.\u003C\u002Fli>\u003Cli>\u003Cstrong style=\"font-weight:bold;color:#1a1a1a;\">Constraints\u003C\u002Fstrong>: Which data cannot be used and which actions cannot be performed. For example: cannot change production state, cannot read unmasked fields, and cannot send customer privacy data externally.\u003C\u002Fli>\u003Cli>\u003Cstrong style=\"font-weight:bold;color:#1a1a1a;\">Output\u003C\u002Fstrong>: What success looks like. For example: classification labels, risk level, to-do summary, and reply draft. It must be quickly verifiable by rules or by humans.\u003C\u002Fli>\u003Cli>\u003Cstrong style=\"font-weight:bold;color:#1a1a1a;\">Exceptions\u003C\u002Fstrong>: What fallback path is taken when materials are missing, tools time out, permissions are insufficient, or confidence is low.\u003C\u002Fli>\u003C\u002Ful>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">If these four items cannot be written clearly, do not connect the Agent yet. Between vague chat and operable automation lies an input\u002Foutput contract.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">2. Use Read-Only Trial Runs to Locate Breakpoints\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Do not let it write to databases, send notifications, or create tasks right away. Start with a read-only trial run: allow reading, not writing; allow suggestions, not execution. Record every action.\u003C\u002Fp>\u003Cpre style=\"background:#f5f7fa;padding:15px;border-radius:6px;font-size:14px;line-height:1.6;overflow-x:auto;margin:1em 0;\">\u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">Read input > Parse constraints > Plan steps > Call read-only tools > Generate candidate result > Self-check > Wait for manual confirmation\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">You can follow this chain to find breakpoints. Problems usually appear in four places: misunderstanding, incorrect planning, tool errors, and insufficient result validation. If the breakpoint is not located, switching models merely changes the way it fails.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">3. Start with a Single Point That Has Clear Boundaries\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Prioritize scenarios with stable inputs, short outputs, and low cost of failure. Examples include ticket classification, material summarization, process reminders, and meeting key point extraction. A complete customer-service closed loop, automatic refunds, and automatic publication of external announcements are not the first step.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Industry observation suggests that Agents that can stably enter production are usually not full-process takeovers, but observable executors of clearly defined tasks.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">\u003Cem>— Draw the map on paper first; only then will there be fewer risks inside the system.\u003C\u002Fem>\u003C\u002Fp>\u003Cp style=\"text-align:center;margin:16px 0 6px 0;\">\u003Cimg src=\"http:\u002F\u002Fmmbiz.qpic.cn\u002Fsz_mmbiz_jpg\u002F4WyfSFibfkRP3wvvjPW4Q00TYfHkuyMlibzpUIuXY3I7KKMfjK6UYFne6fibcczoK0DtgAia6kMVpycqLsvOlibCpXY4nuHfZjJMibKJmYhoWiaZxM\u002F0?from=appmsg\" alt=\"Illustration · Distant Mountains Layering\" style=\"max-width:100%;border-radius:6px;display:block;margin:0 auto;\" \u002F>\u003C\u002Fp>\u003Cp style=\"text-align:center;font-size:12px;color:#bbb;margin:0 0 18px 0;\">Illustration · Distant Mountains Layering\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">II. The Three-Flow Map: Task Flow, Permission Flow, Evidence Flow\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Deployment assessment can start with three diagrams. They mutually constrain one another: the task flow decides what to do, the permission flow decides whether it can be done, and the evidence flow decides whether it can be explained after something goes wrong.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">1. Task Flow: Break the Goal into Verifiable Steps\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The task flow is not a natural-language wish; it consists of process nodes. Each node must have entry conditions, deliverables, verification methods, and failure destinations. Only then can you judge which steps can be automated and which require manual confirmation.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">For example, automatic handling of customer complaints should be broken down into: identifying the request, extracting the order, querying rules, determining responsibility, generating a reply, and submitting for approval. The first three steps can mostly be automated; the last three may require human confirmation; and the final write to the ticketing system must pass through permission boundaries.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">2. Permission Flow: Divide Actions into Three States\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The permission flow must answer: can this tool read, can it write, who approves, and how is it stopped when an error occurs? Do not just hand the Agent a string of API names. Every tool must be labeled with its sensitivity level and scope of impact.\u003C\u002Fp>\u003Ctable style=\"width:100%;border-collapse:collapse;font-size:13px;margin:16px 0;\">\u003Cthead>\u003Ctr>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Action Type\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Typical Tools\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Default Boundary\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Launch Conditions\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Read-only query\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">\u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">read_ticket\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">lookup_policy\u003C\u002Fcode>\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Prioritize low-sensitivity fields and restrict the time range\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Field masking and access logs are in place\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Suggestion generation\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">\u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">summarize\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">draft_reply\u003C\u002Fcode>\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Do not send directly to external systems\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Confidence thresholds and template validation are in place\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Write operations requiring approval\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">\u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">create_ticket\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">send_internal_notice\u003C\u002Fcode>\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Execute only after human confirmation\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">The approval chain is traceable and revocable\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">High-risk and disabled\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">\u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">refund\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">publish_public\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">delete_record\u003C\u002Fcode>\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Disabled by default and reviewed separately\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Multi-factor approval, canary toggle, and robust rollback are in place\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">This table is not meant to restrict innovation, but to keep the Agent running within a controllable range. Automatic execution does not mean no one is accountable; approval does not mean inefficiency; and disabling does not mean backwardness.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">3. Evidence Flow: Make the Process Auditable\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The evidence flow must record why each step occurred. Minimum fields include \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">trace_id\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">task_id\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">step\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">tool\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">input_hash\u003C\u002Fcode>, and \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">output_hash\u003C\u002Fcode>.\u003C\u002Fp>\u003C\u002Fsection>\n\u003Csection class=\"portal-disclaimer\" data-portal-disclaimer=\"1\" style=\"margin-top:2.5em;padding-top:1.25em;border-top:1px solid #e8e4dc;font-size:14px;line-height:1.7;color:#7a756c;\">\u003Cp style=\"margin:0;\">Disclaimer: This article is compiled from publicly available online sources for educational purposes only and does not represent the official position of Guanshan Academy. If you believe your rights have been infringed, please contact us for removal.\u003C\u002Fp>\u003Cp style=\"margin:0.75em 0 0;\">Contact: \u003Ca href=\"mailto:chenxj.g@gmail.com\">chenxj.g@gmail.com\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fsection>","https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Flandscape_mountain.jpg",3491458,"2026-09-29T17:51:07+08:00","2026-09-29T18:17:05.736489+08:00","2026-09-29T21:35:02.047349+08:00",{"id":60,"locale":13,"slug":61,"type":15,"title":62,"summary":63,"content_html":64,"video_url":19,"cover_url":20,"author":21,"category":22,"status":23,"source_job_id":65,"published_at":66,"created_at":67,"updated_at":68},190,"ai-agent落地-任务合同五步法","Deploying AI Agents: The Five-Step Task Contract Method","After reading, you can turn vague requirements into a task contract that specifies inputs, outputs, tool permissions, failure budgets, and rollback lines, so you can start the launch review today.","\u003Csection data-gzh-readable=\"1\" style=\"margin:0;padding:0;\">\u003Cdiv data-gzh-header=\"1\">\u003Cp style=\"font-size:15px;color:#888;text-align:center;margin-top:8px;\">After reading, you can turn vague requirements into a task contract that specifies inputs, outputs, tool permissions, failure budgets, and rollback lines, so you can start the launch review today.\u003C\u002Fp>\n\u003Cfigure class=\"portal-article-figure\">\u003Cimg src=\"https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Freading_books.jpg\" alt=\"读书明理\" loading=\"lazy\" decoding=\"async\" \u002F>\u003Cfigcaption>读书明理\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u003Cp style=\"font-size:14px;color:#999;text-align:center;margin-top:4px;\">Author: Jinxue Studio · Guanshan Academy\u003C\u002Fp>\u003Cp style=\"font-size:13px;color:#999;text-align:center;margin:0 0 6px 0;\">Approximately 3,923 words · About 14 minutes to read\u003C\u002Fp>\u003Chr style=\"border:none;border-top:1px solid #eee;margin:24px 0;\" \u002F>\u003C\u002Fdiv>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Recently, a team building a customer-service agent showed me a demo: input ticket text, output handling suggestions, and it ran smoothly on the spot. But once it was placed into a real ticket pool, problems appeared immediately: Will it modify user profiles? Will it promise refunds to customers? Can errors be reversed? Questions like these are hard to answer with prompts alone. A truly deployable agent first needs a task contract.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">You can think of this contract as the agent’s job description: what it is responsible for, what it can see, what tools it can call, how to roll back when it fails, and who must approve before it touches critical systems. By the end of this article, you will have a five-step launch method, an example set of contract fields, and an architecture-choice comparison table, so you can start the review today.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">\u003Cem>— Let boundaries speak first, then let the model perform.\u003C\u002Fem>\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">1. Define the Agent’s Boundaries First\u003C\u002Fh2>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Split Requirements into Four Categories; Don’t Rush to Connect Tools\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Many requirements start out vague: build an intelligent customer-service assistant, build a report-summarization agent, build a ticket-triage bot. They all sound runnable, but the boundaries have not been split. The first step in deployment is not choosing a model; it is writing down the goal, inputs, outputs, and prohibited actions clearly.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Goals must be verifiable. For example, assign tickets to the correct queue and provide the next-step suggestion. Inputs must be limited. For example, read only user descriptions, historical ticket summaries, and product rules; do not read phone numbers, addresses, or payment information. Outputs must have a fixed format. For example, output \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">JSON\u003C\u002Fcode>, containing category, priority, confidence, and suggested action.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Prohibited Actions Matter More Than Automatic Actions\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Before an agent goes live, the most dangerous thing is not what it cannot do, but what it does on its own. Refunds, sending coupons, modifying orders, writing emails, creating external accounts, and deleting files—these must first be listed as prohibited actions.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">You can divide actions into three tiers: automatic, approval-required, and must-disable. Low-risk actions can be automatic, such as generating summaries, classifying labels, and suggesting talk tracks. Medium- and high-risk actions require approval, such as sending emails, submitting tickets, and creating tasks. High-risk actions are disabled outright, such as refunds, price changes, exporting sensitive data, and deleting records.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Replacing a lengthy PRD with a task contract is not meant to reduce communication, but to let product, engineering, and business confirm on the same page whether this agent can actually go live.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">\u003Cem>— A contract is not a document; it is an executable boundary.\u003C\u002Fem>\u003C\u002Fp>\u003Cp style=\"text-align:center;margin:16px 0 6px 0;\">\u003Cimg src=\"http:\u002F\u002Fmmbiz.qpic.cn\u002Fsz_mmbiz_jpg\u002F4WyfSFibfkRPXG5kVn7Gty6umlEGr75ibyL0fUJgic2SPNpg4aKSl7BBia8MDTsQGRqcMSEh9PiaMVnLp9be3uNYhWN71RIZN3lqiate8ericeOu0Y\u002F0?from=appmsg\" alt=\"Image: Spring Field Soft Light\" style=\"max-width:100%;border-radius:6px;display:block;margin:0 auto;\" \u002F>\u003C\u002Fp>\u003Cp style=\"text-align:center;font-size:12px;color:#bbb;margin:0 0 18px 0;\">Image: Spring Field Soft Light\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">2. Core Fields of the Task Contract\u003C\u002Fh2>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">A Minimum Viable Task Contract\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Contract fields do not need to be long, but they must be executable. Below is a YAML example that can be placed directly in the project repository, suitable for scenarios such as ticket triage and report summarization.\u003C\u002Fp>\u003Cpre style=\"background:#f5f7fa;padding:15px;border-radius:6px;font-size:14px;line-height:1.6;overflow-x:auto;margin:1em 0;\">\u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">contract:  id: ticket-triage-v1  objective: Classify incoming tickets into the correct queues and provide next-step handling suggestions  context:    ticket_pool_snapshot: current_day    knowledge_base_version: rules_v1    business_rules_version: cs_v3  input:    allowed_fields:      - ticket_text      - user_type      - product_name    forbidden_fields:      - phone      - address      - payment_id  output:    format: json    required:      - category      - priority      - confidence      - next_action  tools:    read_only:      - knowledge_base_search      - ticket_history_query    write_allowed: []    approval_required:      - create_followup_ticket  dependencies:    mcp:      allowed_servers:        - ticket_service      read_only: true    function_calling:      whitelist:        - knowledge_base_search        - ticket_history_query  forbidden:    - refund    - price_change    - export_user_data    - delete_ticket  data_boundary:    retention: 24h    redaction:      - email      - phone  failure_budget:    auto_retry: 1    max_error_rate: threshold_by_business  rollback_line:    mode: human_queue    fallback: rule_based_router\u003C\u002Fcode>\u003C\u002Fpre>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Every Field Must Be Acceptance-Testable\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The objective field cannot simply say “improve efficiency.” It must be written as conditions that can be counted from logs and spot-checked from results. For example, the category field must match an enum value; tasks with confidence below the threshold must go to the human queue.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Tool permissions cannot simply say “allow calling the knowledge base.” They must specify which API, which parameters, read-only or write, and whether a \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">trace id\u003C\u002Fcode> is recorded. Dependencies such as \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">MCP\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">Function Calling\u003C\u002Fcode>, and \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">RAG\u003C\u002Fcode> knowledge bases must become verifiable conditions: can it be called, what was called, what was returned, and what happens on failure.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Context fields are equally important. The current ticket-pool snapshot, knowledge-base version, and business-rule version should all be written into the contract. Otherwise, the same type of question may be answered correctly yesterday and incorrectly today, making it hard during troubleshooting to determine whether the issue is caused by the model, the data, or rule drift.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Data boundaries determine the security line. Which fields may enter the model context, which must be redacted, and which cannot be read at all must be hardcoded in the contract. Failure budgets determine the operations line. How many retries are allowed and what fallback triggers when thresholds are exceeded cannot be decided only after errors occur.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Rollback lines determine business confidence. When errors occur, whether to switch to the human queue, downgrade to a rule engine, or pause writes entirely must have a designated owner in advance.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">\u003Cem>— Try read-only first, then enable writes.\u003C\u002Fem>\u003C\u002Fp>\u003Cp style=\"text-align:center;margin:16px 0 6px 0;\">\u003Cimg src=\"http:\u002F\u002Fmmbiz.qpic.cn\u002Fmmbiz_jpg\u002F4WyfSFibfkRPCjzBokF0OezvXu42jvAgJFeR30Y9iczutnzLpx1HMBibeXfadjmGWtSPYAwoAG8JHMwlVQHibK9NXo4x5hBYAWmdNhnNgnM0SJ0\u002F0?from=appmsg\" alt=\"Image: Garden Path\" style=\"max-width:100%;border-radius:6px;display:block;margin:0 auto;\" \u002F>\u003C\u002Fp>\u003Cp style=\"text-align:center;font-size:12px;color:#bbb;margin:0 0 18px 0;\">Image: Garden Path\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">3. The Five-Step Deployment Practice\u003C\u002Fh2>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Step 1: Review the Contract\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Bring product, engineering, and business together for a short meeting and review only the contract fields. Do not discuss model capabilities; first confirm output standards, permission boundaries, and prohibited actions.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">During the review, ask three questions: Who decides when an output is wrong? Who blocks tool calls that exceed boundaries? Can the business side accept the worst-case outcome? If none of the three parties has a clear answer, do not launch yet.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Step 2: Read-Only Pilot\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">A read-only pilot is the safest way to start. The agent can only read data and generate suggestions; it cannot write back to any system. For example, it can only tag tickets, recommend talk tracks for customer-service agents, and generate report outlines.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">During the pilot, focus on three types of evidence: logs that show the full path, metrics that show the error rate, and spot checks that show real business judgment. According to public materials and industry observation, many teams initially hesitate to deploy agents not because the models are not smart enough, but because they lack traceable paths.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Step 3: Gradual Rollout with Approval\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Low-risk actions can be executed automatically. Medium- and high-risk actions go through an approval queue. For example, generating a refund explanation can be automatic, but submitting a refund cannot; creating a follow-up ticket can be automatic, but modifying user contact information must require approval.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The rollout strategy can be opened gradually by business domain, user type, time window, or ticket label. Each expansion must be able to roll back independently.\u003C\u002Fp>\n\u003Csection class=\"portal-disclaimer\" data-portal-disclaimer=\"1\" style=\"margin-top:2.5em;padding-top:1.25em;border-top:1px solid #e8e4dc;font-size:14px;line-height:1.7;color:#7a756c;\">\u003Cp style=\"margin:0;\">Disclaimer: This article is compiled from publicly available online sources for educational purposes only and does not represent the official position of Guanshan Academy. If you believe your rights have been infringed, please contact us for removal.\u003C\u002Fp>\u003Cp style=\"margin:0.75em 0 0;\">Contact: \u003Ca href=\"mailto:chenxj.g@gmail.com\">chenxj.g@gmail.com\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fsection>",3491457,"2026-09-29T17:47:32+08:00","2026-09-29T18:30:42.828261+08:00","2026-09-29T21:35:02.184734+08:00",{"id":70,"locale":13,"slug":71,"type":15,"title":72,"summary":73,"content_html":74,"video_url":19,"cover_url":20,"author":75,"category":22,"status":23,"published_at":76,"created_at":77,"updated_at":78},177,"提示词工程实战-系统提示-few-shot-与结构化输出技巧","Prompt Engineering in Practice: System Prompts, Few-shot Examples, and Structured Output Techniques","This article reviews system prompts, Few-shot examples, and structured output methods in prompt design from an engineering perspective, and provides a practical checklist, common pitfalls, and directions for further reading.","\u003Ch2>Background and Problems\u003C\u002Fh2>\u003Cp>When integrating large models into real business workflows, many teams first encounter uncontrollable outputs: answers drift off-topic, fields are missing, formats become inconsistent, and repeated calls with the same input produce noticeably different results. Prompt engineering is not about finding mysterious incantations; it is about writing task goals, input data, constraints, examples, and output protocols in a form that the model can understand consistently. For developers, it is more like interface documentation for a probabilistic system: it must describe business intent while also designing for exceptions, edge cases, and parsing costs.\u003C\u002Fp>\n\u003Cfigure class=\"portal-article-figure\">\u003Cimg src=\"https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Freading_books.jpg\" alt=\"读书明理\" loading=\"lazy\" decoding=\"async\" \u002F>\u003Cfigcaption>读书明理\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u003Ch2>Core Concepts\u003C\u002Fh2>\u003Ch3>System Prompts\u003C\u002Fh3>\u003Cp>A system prompt is usually used to define the model's role, capability boundaries, tone, safety policies, and output conventions. For example, you can ask the model to act as a customer service representative, a code reviewer, or a data extraction assistant. A good system prompt should clearly state what must be done, what must not be done, and what to do when uncertain. Public documentation indicates that different models handle system messages differently and may assign them different priorities. In production environments, follow the official documentation and verify behavior through regression testing.\u003C\u002Fp>\u003Ch3>Few-shot Examples\u003C\u002Fh3>\u003Cp>Few-shot prompting uses a small number of examples to show the model the task pattern. It is suitable for classification, entity extraction, sentiment analysis, ticket routing, content rewriting, and similar tasks. Examples do more than show the correct answer; more importantly, they show decision boundaries. Normal samples, ambiguous samples, missing-data samples, and samples that should be refused can all be included. The closer the examples are to the real input distribution, the more easily the model can reuse them consistently.\u003C\u002Fp>\u003Ch3>Structured Output\u003C\u002Fh3>\u003Cp>Structured output emphasizes having the model return results that programs can parse, such as JSON, fixed field lists, CSV, or objects with a schema. Compared with free text, structured output can significantly reduce post-processing costs. In real projects, you can require the model to output only JSON and specify field names, types, enumerated values, and null-handling policies. If the model platform provides function calling, tool calling, or JSON Schema constraints, prefer those platform capabilities.\u003C\u002Fp>\u003Cp>From an engineering perspective, the three can be combined: system prompts stabilize the persona and baseline rules, Few-shot examples calibrate task format and judgment criteria, and structured output connects to downstream programs. If you only write “Please help me process this,” the model can easily drift in style, fields, and boundary handling.\u003C\u002Fp>\u003Ch2>Practical Steps and Checklist\u003C\u002Fh2>\u003Cul>\u003Cli>\u003Cstrong>Start with the task description:\u003C\u002Fstrong> Describe in one sentence what the input is, what the output is, and what success looks like.\u003C\u002Fli>\u003Cli>\u003Cstrong>Separate roles and rules:\u003C\u002Fstrong> Put long-lived stable constraints in the system prompt, and place variable data for the current request in the user message.\u003C\u002Fli>\u003Cli>\u003Cstrong>Design Few-shot examples:\u003C\u002Fstrong> Start with 2 to 5 examples, covering typical scenarios and at least one edge case.\u003C\u002Fli>\u003Cli>\u003Cstrong>Define the output protocol:\u003C\u002Fstrong> Specify fields, types, required fields, enumerated values, and what to return when the model cannot determine an answer.\u003C\u002Fli>\u003Cli>\u003Cstrong>Control context length:\u003C\u002Fstrong> Keep only fields, examples, and rules relevant to the current task to avoid interference from unrelated information.\u003C\u002Fli>\u003Cli>\u003Cstrong>Add a validation layer:\u003C\u002Fstrong> On the application side, parse the returned content as JSON, validate fields, and retry when needed; do not assume the model output is always valid.\u003C\u002Fli>\u003Cli>\u003Cstrong>Build an evaluation set:\u003C\u002Fstrong> Use fixed samples to measure format validity rate, field accuracy, refusal accuracy, and cost.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Below is a simplified prompt structure example for extracting issue type and urgency from user feedback:\u003C\u002Fp>\u003Cpre>\u003Ccode>system: You are a ticket classification assistant. Output only JSON, with no explanation. Field requirements: category must be bug, billing, account, or other; urgency must be low, medium, or high; summary must be no more than 20 characters. user: User feedback: {feedback} assistant: {"category":"billing","urgency":"medium","summary":"Duplicate charge"}\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp>This example places the role, output fields, enumerated values, and sample output in the same testable template. In actual use, {feedback} can be replaced by the program with real text, and the returned JSON can then be validated against a schema.\u003C\u002Fp>\u003Ch2>Common Pitfalls and Suggestions\u003C\u002Fh2>\u003Cul>\u003Cli>\u003Cstrong>Conflicting instructions:\u003C\u002Fstrong> For example, requiring both “extremely concise answers” and “list all supporting evidence in detail.” Make priorities explicit and, when necessary, produce output in stages.\u003C\u002Fli>\u003Cli>\u003Cstrong>Overly idealized examples:\u003C\u002Fstrong> If all Few-shot examples are clean inputs, the model may fail when encountering noisy text. Add dirty data, short texts, and ambiguous samples.\u003C\u002Fli>\u003Cli>\u003Cstrong>Relying only on prompts to prevent injection:\u003C\u002Fstrong> System prompts cannot replace a security layer. User input may contain phrases like “ignore previous instructions,” so input filtering, permission isolation, and output validation are also needed.\u003C\u002Fli>\u003Cli>\u003Cstrong>Ignoring sampling parameters:\u003C\u002Fstrong> Extraction, classification, and structured-output tasks are usually better with lower randomness, while creative generation can allow more randomness. Parameter names and effects vary by platform, so follow the official documentation.\u003C\u002Fli>\u003Cli>\u003Cstrong>Treating long prompts as a universal solution:\u003C\u002Fstrong> Too many rules can dilute the key points. Put stable rules in the system prompt, dynamic data in the user message, and organize content with subheadings or delimiters.\u003C\u002Fli>\u003Cli>\u003Cstrong>No failure fallback:\u003C\u002Fstrong> When the model returns invalid JSON, record the raw response, trigger a retry, or fall back to human review or rule-based parsing.\u003C\u002Fli>\u003Cli>\u003Cstrong>Missing evaluation metrics:\u003C\u002Fstrong> Judging prompt quality only by subjective impression can break after model upgrades. Record each prompt version, test-set results, and failure cases from production.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Directions for Further Reading\u003C\u002Fh2>\u003Cp>After mastering basic prompt engineering, you can further explore context engineering, retrieval-augmented generation (RAG), agent tool calling, model evaluation systems, prompt version management, caching and cost control, and prompt injection protection. If a team is preparing to launch a production system, treat prompts as code assets: include them in version control, unit testing, canary releases, and online monitoring. In this way, prompt engineering can evolve from one-off tuning into a sustainable engineering capability.\u003C\u002Fp>\n\u003Csection class=\"portal-sources\" style=\"margin-top:2em;font-size:14px;line-height:1.7;color:#5c5850;\">\u003Cp style=\"margin:0 0 0.5em;font-weight:600;\">References\u003C\u002Fp>\u003Cul style=\"margin:0;padding-left:1.25em;\">\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fwww.runoob.com\u002Fai-agent\u002Fprompt-engineering.html\" rel=\"noopener noreferrer\" target=\"_blank\">提示词工程（Prompt Engineering） | 菜鸟教程\u003C\u002Fa>\u003C\u002Fli>\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fwww.cnblogs.com\u002Fluzhanshi\u002Farticles\u002F19071865\" rel=\"noopener noreferrer\" target=\"_blank\">提示词工程（Prompt Engineering）完全指南：从入门到生产 ...\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Ful>\u003C\u002Fsection>\n\u003Csection class=\"portal-disclaimer\" data-portal-disclaimer=\"1\" style=\"margin-top:2.5em;padding-top:1.25em;border-top:1px solid #e8e4dc;font-size:14px;line-height:1.7;color:#7a756c;\">\u003Cp style=\"margin:0;\">Disclaimer: This article is compiled from publicly available online sources for educational purposes only and does not represent the official position of Guanshan Academy. If you believe your rights have been infringed, please contact us for removal.\u003C\u002Fp>\u003Cp style=\"margin:0.75em 0 0;\">Contact: \u003Ca href=\"mailto:chenxj.g@gmail.com\">chenxj.g@gmail.com\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fsection>","Guanshan Academy","2026-09-29T16:26:23.505014+08:00","2026-09-29T16:29:26.228273+08:00","2026-09-29T21:35:02.016563+08:00",{"id":80,"locale":13,"slug":81,"type":15,"title":82,"summary":83,"content_html":84,"video_url":19,"cover_url":85,"author":75,"category":22,"status":23,"published_at":86,"created_at":87,"updated_at":88},170,"ai-安全与对齐工程实践-幻觉控制-越狱防护与内容审核","AI Safety and Alignment Engineering in Practice: Hallucination Control, Jailbreak Prevention, and Content Moderation","From an engineering implementation perspective, this article reviews the core issues of large model safety and alignment. It outlines layered defenses, red-team testing, and launch checklists for hallucination control, jailbreak prevention, and content moderation, helping developers build more reliable generative AI applications.","\u003Ch2>Background and Problems\u003C\u002Fh2>\n\u003Cp>Over the past two years, large models have moved from demo tools into production systems such as customer service, R&D assistants, knowledge Q&A, and marketing content generation. Once connected to real business workflows, problems quickly become concrete: a model may invent nonexistent API parameters, produce prohibited content after multiple rounds of prompting, or treat hidden text on a web page as an instruction to execute. For engineering teams, AI safety and alignment are not abstract ethical slogans, but a set of engineering constraints that must be incorporated into requirements, testing, release, and operations.\u003C\u002Fp>\n\u003Cfigure class=\"portal-article-figure\">\u003Cimg src=\"https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Ftea_garden.jpg\" alt=\"茶园春色\" loading=\"lazy\" decoding=\"async\" \u002F>\u003Cfigcaption>茶园春色\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u003Cp>Common risks can be summarized in three categories: first, hallucinations, where the model generates content that sounds plausible but lacks factual basis; second, jailbreaks and prompt injection, where attackers bypass safety policies through phrasing, encoding, role-playing, or external data; third, content compliance risks, including illegal content, violence, discrimination, privacy leaks, and high-risk professional advice. A truly mature system does not assume the model is always correct; instead, it uses multiple layers of mechanisms to make errors discoverable, blockable, and traceable.\u003C\u002Fp>\n\u003Ch2>Core Concepts\u003C\u002Fh2>\n\u003Ch3>Safety, Alignment, and Controllability\u003C\u002Fh3>\n\u003Cul>\n\u003Cli>\u003Cstrong>Safety\u003C\u002Fstrong>: The system avoids producing harmful, illegal, or misleading outputs as much as possible across diverse inputs.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Alignment\u003C\u002Fstrong>: Model behavior remains consistent with user intent, product goals, organizational policies, and social norms.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Controllability\u003C\u002Fstrong>: Developers can define the boundaries of model capabilities, audit key behaviors, and stop or degrade service when anomalies occur.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>The relationship among the three can be understood as follows: alignment addresses what the model should do, safety addresses what the model must not do, and controllability addresses how humans can supervise and correct it. If there are only prompts without permission boundaries, the model can easily be manipulated; if there are only filters without factual grounding, the system may easily block legitimate requests.\u003C\u002Fp>\n\u003Ch3>Hallucination Control\u003C\u002Fh3>\n\u003Cp>Hallucination does not mean the model is lying; it is more like the model continuing to speak with high confidence even when information is insufficient. In engineering practice, control usually comes from three directions: first, reduce unsupported generation, for example by introducing retrieval augmentation, knowledge base citations, and tool queries; second, lower confidence in uncertain answers, for example by requiring the model to distinguish facts, speculation, and unknowns; third, establish verification mechanisms, such as citation validation, answer consistency checks, and human spot checks.\u003C\u002Fp>\n\u003Cp>For fact-based tasks, it is advisable to divide answers into verifiable and unverifiable categories. Verifiable questions should go through retrieval or databases whenever possible, while unverifiable questions should clearly indicate uncertainty. This is more effective than simply telling the model not to hallucinate.\u003C\u002Fp>\n\u003Ch3>Jailbreak Prevention\u003C\u002Fh3>\n\u003Cp>Jailbreaks usually exploit the model’s generalization ability in natural language, for example by pretending to write fiction, debug code, discuss academic topics, or act as a system administrator, or by using Base64, Unicode, multilingual mixing, and other techniques to evade detection. Prompt injection goes a step further by hiding malicious instructions in user input, web page content, document attachments, or tool responses.\u003C\u002Fp>\n\u003Cp>The focus of prevention is not to find a universal keyword list, but to build layered defenses: the input layer identifies high-risk intents, the system layer separates user instructions from system policies, the tool layer restricts permissions for files, network access, databases, and code execution, the output layer performs safety review, and the logging layer preserves auditable evidence.\u003C\u002Fp>\n\u003Ch3>Content Moderation\u003C\u002Fh3>\n\u003Cp>Content moderation is the last line of defense before model outputs reach users. It should not be just a black-box classifier; it should include clear policies: which content must be blocked, which content needs rewriting or downgrading, which content can be allowed with warnings, and which scenarios must be escalated to humans. Moderation policies should match business risk levels. For example, scenarios involving healthcare, legal advice, finance, or minors should be significantly stricter.\u003C\u002Fp>\n\u003Ch2>Practical Steps and Checklists\u003C\u002Fh2>\n\u003Ch3>1. Define Risk Levels First, Then Choose Model Capabilities\u003C\u002Fh3>\n\u003Cul>\n\u003Cli>Clarify whether the application involves funds, healthcare, legal advice, minors, privacy, or automated execution.\u003C\u002Fli>\n\u003Cli>Based on the risk level, decide whether internet access is allowed, whether tool calls are allowed, and whether executable code generation is allowed.\u003C\u002Fli>\n\u003Cli>For high-risk scenarios, set up human review, rate limiting, allowlists, and strong auditing.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch3>2. Build a Layered Defense Pipeline\u003C\u002Fh3>\n\u003Cp>A common production pipeline includes input cleaning, intent recognition, permission checks, retrieval augmentation, model generation, fact checking, output moderation, and logging. Below is a simplified pseudocode example showing where each checkpoint can be placed.\u003C\u002Fp>\n\u003Cpre>\u003Ccode>def guardrail(prompt, context):\n    if contains_secret(prompt):\n        return deny('Do not submit secrets or sensitive personal information')\n\n    intent = classify_intent(prompt)\n    if intent in ('medical', 'legal', 'finance'):\n        return answer_with_disclaimer(prompt, require_review=True)\n\n    if intent == 'fact':\n        context = retrieve_sources(prompt)\n\n    answer = llm_generate(prompt, context, temperature=0.2)\n\n    if intent == 'fact' and not has_citation(answer):\n        answer = add_uncertainty_note(answer)\n\n    return output_filter(answer)\n\u003C\u002Fcode>\u003C\u002Fpre>\n\u003Cp>This code is not a complete safety solution, but it illustrates the engineering approach: first handle sensitive information and high-risk intents, then decide whether retrieval is needed based on task type, and finally moderate the output and add uncertainty notes where appropriate.\u003C\u002Fp>\n\u003Ch3>3. Key Actions for Hallucination Control\u003C\u002Fh3>\n\u003Cul>\n\u003Cli>Connect fact-based questions to retrieval, knowledge bases, or structured queries, and require source citations whenever possible.\u003C\u002Fli>\n\u003Cli>Restrict the model from freely improvising when evidence is lacking; configure refusal templates or escalation to humans.\u003C\u002Fli>\n\u003Cli>Use chunked retrieval for long-document Q&A to prevent the model from stitching together incorrect conclusions across sections.\u003C\u002Fli>\n\u003Cli>Build offline evaluation sets, focusing on factual accuracy, citation hit rate, and the reasonableness of refusals.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch3>4. Jailbreak and Prompt Injection Testing\u003C\u002Fh3>\n\u003Cul>\n\u003Cli>Create test cases involving role-playing, reverse instructions, multi-turn manipulation, encoding bypasses, and multilingual mixing.\u003C\u002Fli>\n\u003Cli>Check whether system prompts can be leaked and whether tool calls can be indirectly controlled by users.\u003C\u002Fli>\n\u003Cli>Isolate external content from web scraping, file parsing, and database responses to prevent indirect injection.\u003C\u002Fli>\n\u003Cli>Run regression red-team testing after every model upgrade, prompt adjustment, or tool change.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch3>5. Content Moderation and Operational Closed Loop\u003C\u002Fh3>\n\u003Cul>\n\u003Cli>Output moderation should cover illegal content, violence, discrimination, privacy, self-harm, minors, and high-risk professional advice.\u003C\u002Fli>\n\u003Cli>Record reasons for blocked cases, review false positives, and continuously update policies.\u003C\u002Fli>\n\u003Cli>Provide specialized response scripts and human escalation paths for scenarios such as customer service, education, and healthcare.\u003C\u002Fli>\n\u003Cli>Store logs after data masking, and clearly define retention periods and access permissions in accordance with applicable laws and official compliance requirements.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2>Common Pitfalls and Recommendations\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>\u003Cstrong>Relying only on system prompts\u003C\u002Fstrong>: System prompts are important, but they cannot be the only line of defense. Attackers can change model behavior through multi-turn conversations or external content, so permission controls and output moderation are also required.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Oversimplified keyword filtering\u003C\u002Fstrong>: Keyword lists can easily be bypassed with synonyms, pinyin, encoding, or metaphors. Use semantic classification, contextual judgment, and risk tiering.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Attributing hallucinations only to prompts\u003C\u002Fstrong>: Without reliable knowledge sources and evaluation mechanisms, simply asking the model to answer cautiously often has limited effect.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Excessive refusals harming usability\u003C\u002Fstrong>: Safety policies should not block everything. For edge cases, provide explanations, alternatives, or human support to avoid repeatedly rejecting users.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Ignoring indirect injection\u003C\u002Fstrong>: When the model reads web pages, PDFs, emails, or tool responses, external text may carry instructions. Clearly separate data content from executable instructions.\u003C\u002Fli>\n\u003Cli>\u003Cstrong>No continuous evaluation\u003C\u002Fstrong>: Model versions, prompts, knowledge bases, and tools all change. Without regression testing, safety capabilities can quietly degrade over iterations.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2>Directions for Further Reading\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>OWASP security risk lists for large model applications and generative AI security practices.\u003C\u002Fli>\n\u003Cli>Public governance frameworks such as the NIST AI Risk Management Framework.\u003C\u002Fli>\n\u003Cli>According to publicly available materials, safety alignment and red-team testing methods proposed by institutions such as Google SAIF and Anthropic.\u003C\u002Fli>\n\u003Cli>Alignment technical routes such as RLHF, DPO, and Constitutional AI, along with their applicable boundaries.\u003C\u002Fli>\n\u003Cli>RAG evaluation metrics such as faithfulness, answer relevance, citation accuracy, and refusal rate.\u003C\u002Fli>\n\u003Cli>Automated red-teaming, adversarial prompt generation, and model behavior monitoring toolchains.\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>Overall, AI safety and alignment are not one-time projects, but ongoing operations. Engineering teams need to bring hallucination control, jailbreak prevention, and content moderation into the same quality system, managing model risk in ways that are testable, observable, and rollback-capable.\u003C\u002Fp>\n\u003Csection class=\"portal-sources\" style=\"margin-top:2em;font-size:14px;line-height:1.7;color:#5c5850;\">\u003Cp style=\"margin:0 0 0.5em;font-weight:600;\">References\u003C\u002Fp>\u003Cul style=\"margin:0;padding-left:1.25em;\">\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fwww.chagushici.com\u002Fzidian\u002Fzuci\u002F%E5%A4%A7\" rel=\"noopener noreferrer\" target=\"_blank\">大组词_大字组词_大的词语 - 汉语词典\u003C\u002Fa>\u003C\u002Fli>\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F636078956\" rel=\"noopener noreferrer\" target=\"_blank\">AI安全与对齐: When, Why, What, and How - 知乎\u003C\u002Fa>\u003C\u002Fli>\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fnotes.hehugo.com\u002Fresearch\u002Fai\u002Fengineering\u002Fai-safety-alignment-guide\" rel=\"noopener noreferrer\" target=\"_blank\">AI 安全与对齐完全指南 | 何雨果的知识库\u003C\u002Fa>\u003C\u002Fli>\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F666115472\" rel=\"noopener noreferrer\" target=\"_blank\">AI安全前沿 #1 | AI安全四大抓手：对齐、鲁棒性、监测 ...\u003C\u002Fa>\u003C\u002Fli>\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fblog.csdn.net\u002Fmieshizhishou\u002Farticle\u002Fdetails\u002F140318746\" rel=\"noopener noreferrer\" target=\"_blank\">【有啥问啥】LLM大模型应用中的安全对齐的简单理解 ...\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Ful>\u003C\u002Fsection>\n\u003Csection class=\"portal-disclaimer\" data-portal-disclaimer=\"1\" style=\"margin-top:2.5em;padding-top:1.25em;border-top:1px solid #e8e4dc;font-size:14px;line-height:1.7;color:#7a756c;\">\u003Cp style=\"margin:0;\">Disclaimer: This article is compiled from publicly available online sources for educational purposes only and does not represent the official position of Guanshan Academy. If you believe your rights have been infringed, please contact us for removal.\u003C\u002Fp>\u003Cp style=\"margin:0.75em 0 0;\">Contact: \u003Ca href=\"mailto:chenxj.g@gmail.com\">chenxj.g@gmail.com\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fsection>","https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Ftea_garden.jpg","2026-09-29T14:25:47.577226+08:00","2026-09-29T14:30:35.364115+08:00","2026-09-29T21:35:03.202401+08:00",{"id":90,"locale":13,"slug":91,"type":15,"title":92,"summary":93,"content_html":94,"video_url":19,"cover_url":20,"author":75,"category":22,"status":23,"published_at":95,"created_at":96,"updated_at":97},168,"大语言模型入门-transformer-上下文窗口与提示词基础","Getting Started with Large Language Models: Transformers, Context Windows, and Prompt Basics","This article walks developers through the fundamentals of large language models: how Transformers process text with attention, how context windows shape input and output budgets, and how to craft prompts that express tasks consistently. It includes a practical checklist, common pitfalls, and directions for further reading.","\u003Ch2>Background and Problems\u003C\u002Fh2>\u003Cp>When many developers first encounter large language models, they can easily get confused by concepts such as parameter scale, tokens, context windows, temperature, and system prompts. In real implementations, the problem is often not whether the model is smart enough, but whether we have clearly defined the task boundaries: which text can the model see in a single call? Which information must be retained? How should the output format be constrained? How should errors be handled? From an engineering perspective, this article clarifies three fundamental but critical topics: how Transformers work, the limits of context windows, and basic methods for prompt design.\u003C\u002Fp>\n\u003Cfigure class=\"portal-article-figure\">\u003Cimg src=\"https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Freading_books.jpg\" alt=\"读书明理\" loading=\"lazy\" decoding=\"async\" \u002F>\u003Cfigcaption>读书明理\u003C\u002Ffigcaption>\u003C\u002Ffigure>\u003Ch2>Core Concepts\u003C\u002Fh2>\u003Ch3>Transformer: Understanding Sequences with Attention\u003C\u002Fh3>\u003Cp>The Transformer is a deep learning architecture introduced in the 2017 paper \u003Cstrong>Attention Is All You Need\u003C\u002Fstrong>. It has a notable difference from earlier common RNNs and LSTMs: instead of forcing word-by-word processing in time steps, it uses self-attention to observe multiple positions in a sequence simultaneously and compute the relationships between them. This makes training easier to parallelize and better suited for modeling long-range dependencies.\u003C\u002Fp>\u003Cp>In large language models, text is first split into tokens. Tokens may be words, subwords, or symbols, depending on the tokenizer. Then each token is mapped to a vector and augmented with positional encoding so the model knows the order. After that, the vectors pass through multiple layers of attention and feed-forward networks, progressively refining contextual relationships. Many modern generative models adopt a decoder-only architecture, predicting the next token based on existing text. According to public materials, models such as GPT and LLaMA are based on or improve upon the Transformer architecture; refer to official documentation for specific implementations.\u003C\u002Fp>\u003Cp>When understanding Transformers, focus on several key components:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Token Embedding\u003C\u002Fstrong>: Converts discrete text into continuous vectors.\u003C\u002Fli>\u003Cli>\u003Cstrong>Positional Encoding\u003C\u002Fstrong>: Adds word-order information; otherwise the model only knows the set of contents, not their sequence.\u003C\u002Fli>\u003Cli>\u003Cstrong>Self-Attention\u003C\u002Fstrong>: Uses Query, Key, and Value to determine which positions are more relevant.\u003C\u002Fli>\u003Cli>\u003Cstrong>Multi-Head Attention\u003C\u002Fstrong>: Uses multiple attention groups to capture relationships from different dimensions.\u003C\u002Fli>\u003Cli>\u003Cstrong>Feed-Forward Networks, Residual Connections, and Layer Normalization\u003C\u002Fstrong>: Improve expressive power and help stabilize training.\u003C\u002Fli>\u003C\u002Ful>\u003Cpre>\u003Ccode>scores = Q @ K.T \u002F sqrt(d_k); weights = softmax(scores); output = weights @ V\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp>This pseudocode is not a complete model; it only shows how attention is computed: first use Q and K to obtain relevance scores, then convert them into weights with softmax, and finally compute a weighted sum of V. For engineers, this abstraction is enough to explain why models can select important information based on context.\u003C\u002Fp>\u003Ch3>Context Window: The Range of Tokens a Model Can Reference at Once\u003C\u002Fh3>\u003Cp>The context window is often called context length, max tokens, or maximum context length. It limits the total number of tokens occupied by both input and output in a single request. For example, a window of 8,192 tokens does not mean you can blindly insert 8,192 Chinese characters or English words, because different tokenizers produce different splits. Chinese text, code, tables, and Markdown symbols can all affect token counts.\u003C\u002Fp>\u003Cp>A more robust engineering approach is to treat the context window as a budget rather than a capacity ceiling. System prompts, user input, retrieval results, conversation history, tool outputs, and expected responses should all be included in the budget. For long-document question answering, you usually need chunking, summarization, or retrieval augmentation instead of stuffing the entire document into the model at once.\u003C\u002Fp>\u003Ch3>Prompts: Writing Task Constraints as Model-Executable Input\u003C\u002Fh3>\u003Cp>A prompt is not just a simple question; it is the input specification for a reasoning task. A stable prompt usually includes role, task, context, constraints, output format, and necessary examples. For tasks such as classification, extraction, summarization, and code generation, the closer the prompt is to a clear, testable requirements document, the more stable the model output will be.\u003C\u002Fp>\u003Ch2>Practical Steps or Checklist\u003C\u002Fh2>\u003Cul>\u003Cli>\u003Cstrong>Identify the task type\u003C\u002Fstrong>: First determine whether it is open-ended generation, structured extraction, classification, or code completion. Different tasks have different requirements for temperature and examples.\u003C\u002Fli>\u003Cli>\u003Cstrong>Provide the minimum necessary context\u003C\u002Fstrong>: Keep only the material directly related to the task, and avoid irrelevant text diluting key instructions.\u003C\u002Fli>\u003Cli>\u003Cstrong>Split long text\u003C\u002Fstrong>: If it exceeds the window or approaches the limit, chunk and summarize first, then aggregate or use retrieval augmentation.\u003C\u002Fli>\u003Cli>\u003Cstrong>Fix the output format\u003C\u002Fstrong>: Require output as a list, table, JSON, or fixed fields, and provide field descriptions.\u003C\u002Fli>\u003Cli>\u003Cstrong>Add few-shot examples\u003C\u002Fstrong>: Use one to three examples to calibrate tone, granularity, and edge cases.\u003C\u002Fli>\u003Cli>\u003Cstrong>Reserve space for output\u003C\u002Fstrong>: Do not fill the input completely; leave room for generation and formatting.\u003C\u002Fli>\u003Cli>\u003Cstrong>Run regression tests\u003C\u002Fstrong>: Prepare a set of typical cases and revalidate after every prompt or model version change.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>A simple prompt template can be organized like this:\u003C\u002Fp>\u003Cpre>\u003Ccode>template = 'Role: Technical documentation assistant. Task: Generate a summary from the given text. Requirements: Output 3 key points, each no more than 20 words. Constraints: Do not fabricate facts, do not output irrelevant content. Text: {text}'\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp>This example illustrates a structured way to write prompts: role, task, requirements, constraints, and input data are clearly separated, making it easier for programs to assemble and maintain later.\u003C\u002Fp>\u003Ch2>Common Pitfalls and Suggestions\u003C\u002Fh2>\u003Cul>\u003Cli>\u003Cstrong>Treating the context window as permanent memory\u003C\u002Fstrong>: The model cannot see information outside the window. Long conversations require summarization, external storage, or retrieval mechanisms.\u003C\u002Fli>\u003Cli>\u003Cstrong>Confusing tokens with characters\u003C\u002Fstrong>: A Chinese character, an English word, or a code symbol is not necessarily equal to one token. Use the actual tokenizer to count.\u003C\u002Fli>\u003Cli>\u003Cstrong>Losing focus because the prompt is too long\u003C\u002Fstrong>: Place key constraints at the beginning and end, and present them as lists.\u003C\u002Fli>\u003Cli>\u003Cstrong>Only tuning parameters without fixing the input\u003C\u002Fstrong>: Sampling parameters such as temperature and top_p affect randomness, but when output is unstable, first check the prompt, context, and task boundaries.\u003C\u002Fli>\u003Cli>\u003Cstrong>Ignoring hallucinations and validation\u003C\u002Fstrong>: Models may generate fluent but incorrect content. For decisions involving facts, healthcare, legal matters, finance, or production environments, add retrieval, rule validation, human review, or refusal mechanisms.\u003C\u002Fli>\u003Cli>\u003Cstrong>Blindly pursuing larger windows\u003C\u002Fstrong>: Longer context is not always better; cost, latency, and information noise all increase. If a small context can solve the problem stably, it is usually more economical.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Directions for Further Reading\u003C\u002Fh2>\u003Cul>\u003Cli>\u003Cstrong>The original Transformer paper\u003C\u002Fstrong>: Understand self-attention, encoder, and decoder design.\u003C\u002Fli>\u003Cli>\u003Cstrong>Tokenizers and BPE\u003C\u002Fstrong>: Learn about token counts, vocabulary size, and multilingual processing differences.\u003C\u002Fli>\u003Cli>\u003Cstrong>RAG and long-document processing\u003C\u002Fstrong>: Study retrieval-augmented generation, chunking strategies, and reranking.\u003C\u002Fli>\u003Cli>\u003Cstrong>Structured output\u003C\u002Fstrong>: Explore JSON Schema, function calling, and constrained decoding.\u003C\u002Fli>\u003Cli>\u003Cstrong>Model evaluation\u003C\u002Fstrong>: Build comprehensive metrics covering accuracy, hallucination rate, latency, cost, and user feedback.\u003C\u002Fli>\u003C\u002Ful>\n\u003Csection class=\"portal-sources\" style=\"margin-top:2em;font-size:14px;line-height:1.7;color:#5c5850;\">\u003Cp style=\"margin:0 0 0.5em;font-weight:600;\">References\u003C\u002Fp>\u003Cul style=\"margin:0;padding-left:1.25em;\">\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F338817680\" rel=\"noopener noreferrer\" target=\"_blank\">Transformer模型详解（图解最完整版） - 知乎\u003C\u002Fa>\u003C\u002Fli>\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fwww.runoob.com\u002Fpytorch\u002Ftransformer-model.html\" rel=\"noopener noreferrer\" target=\"_blank\">Transformer 模型 - 菜鸟教程\u003C\u002Fa>\u003C\u002Fli>\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fblog.csdn.net\u002Fweixin_42475060\u002Farticle\u002Fdetails\u002F121101749\" rel=\"noopener noreferrer\" target=\"_blank\">【超详细】【原理篇&实战篇】一文读懂Transformer-CSDN博客\u003C\u002Fa>\u003C\u002Fli>\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F47812375\" rel=\"noopener noreferrer\" target=\"_blank\">[整理] 聊聊 Transformer - 知乎\u003C\u002Fa>\u003C\u002Fli>\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fwww.zhihu.com\u002Ftardis\u002Fzm\u002Fart\u002F600773858\" rel=\"noopener noreferrer\" target=\"_blank\">一文了解Transformer全貌（图解Transformer）\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Ful>\u003C\u002Fsection>\n\u003Csection class=\"portal-disclaimer\" data-portal-disclaimer=\"1\" style=\"margin-top:2.5em;padding-top:1.25em;border-top:1px solid #e8e4dc;font-size:14px;line-height:1.7;color:#7a756c;\">\u003Cp style=\"margin:0;\">Disclaimer: This article is compiled from publicly available online sources for educational purposes only and does not represent the official position of Guanshan Academy. If you believe your rights have been infringed, please contact us for removal.\u003C\u002Fp>\u003Cp style=\"margin:0.75em 0 0;\">Contact: \u003Ca href=\"mailto:chenxj.g@gmail.com\">chenxj.g@gmail.com\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fsection>","2026-09-29T14:05:48.096495+08:00","2026-09-29T14:10:06.126961+08:00","2026-09-29T22:15:37.517446+08:00",{"id":99,"locale":13,"slug":100,"type":15,"title":101,"summary":102,"content_html":103,"video_url":19,"cover_url":54,"author":75,"category":22,"status":23,"published_at":104,"created_at":105,"updated_at":106},156,"大语言模型入门-从-transformer-上下文窗口到提示词基础","Getting Started with Large Language Models: From Transformers and Context Windows to Prompt Basics","This article explains three foundational concepts of large language models for developers: the attention mechanism in Transformers, the engineering boundaries of context windows, and prompt design methods, with a practical checklist and common pitfalls.","\u003Ch2>Background and Problems\u003C\u002Fh2>\u003Cp>Many developers, when first encountering a large language model, tend to treat it as a “smarter API”: send in some text and wait for an answer. Once used in production, problems quickly appear: Why does the model forget earlier requirements? Why does pasting a long document lead to irrelevant answers? Why do different prompts produce completely different results for the same question? To understand these phenomena, three foundational concepts are essential: \u003Cstrong>Transformer\u003C\u002Fstrong>, \u003Cstrong>context window\u003C\u002Fstrong>, and \u003Cstrong>prompting\u003C\u002Fstrong>.\u003C\u002Fp>\n\u003Cfigure class=\"portal-article-figure\">\u003Cimg src=\"https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Flandscape_mountain.jpg\" alt=\"观山静思\" loading=\"lazy\" decoding=\"async\" \u002F>\u003Cfigcaption>观山静思\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u003Cp>This article is aimed at developers and AI practitioners, using an engineering perspective to explain: how large language models process text, how much visible scope a single request has, and how we should organize input so the model can complete tasks more reliably.\u003C\u002Fp>\u003Ch2>Core Concepts\u003C\u002Fh2>\u003Ch3>Transformer: The Mainstream Backbone of Large Language Models\u003C\u002Fh3>\u003Cp>According to public information, most current mainstream large language models are based on the Transformer architecture. Its core breakthrough is not mysterious: instead of processing words sequentially like early recurrent networks, it uses a \u003Cstrong>self-attention mechanism\u003C\u002Fstrong> to compute relationships among all words in a sequence at once.\u003C\u002Fp>\u003Cp>Self-attention can be understood as a “lookup and weighting” process: each word generates three types of vectors—Query, Key, and Value. The model computes attention scores from the similarity between Query and Key, then uses these scores to perform a weighted sum of Value. In this way, every position in a sentence can reference information from other positions.\u003C\u002Fp>\u003Cpre>\u003Ccode># Minimal illustration: score calculation in self-attention\nscores = Q @ K.T \u002F (d_k ** 0.5)\nweights = softmax(scores)\noutput = weights @ V\n\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp>This pseudocode is not meant to implement a complete model, but to show that attention is essentially a set of learnable weights that determine which parts of the context the model focuses on for the current task. A real Transformer also includes modules such as multi-head attention, positional encoding, residual connections, layer normalization, and feed-forward networks. The original paper commonly describes encoder and decoder structures, while many current conversational models more often use a decoder-only autoregressive structure, predicting the next token one by one based on preceding text. Different models vary in architectural details; refer to official documentation for specifics.\u003C\u002Fp>\u003Ch3>Context Window: The Range the Model Can See at Once\u003C\u002Fh3>\u003Cp>A context window usually refers to the maximum number of tokens a model can process in a single inference. Note that this refers to tokens, which do not exactly correspond to Chinese characters, words, or characters. In Chinese scenarios, one Chinese character may correspond to one or more tokens, depending on the tokenizer.\u003C\u002Fp>\u003Cp>From an engineering perspective, the context window should be understood as “working memory,” not “long-term memory.” If the combined history, system instructions, retrieved materials, and user question exceed the window, the input is usually truncated or causes an error; even if it does not exceed the limit, too much information can dilute key content.\u003C\u002Fp>\u003Ch3>Prompts: Input Organization, Not Incantations\u003C\u002Fh3>\u003Cp>A prompt is not just “asking a question”; it is the structured design of the entire input. Common inputs include system instructions, background materials, user questions, examples, and output format constraints. Good prompts are usually not piles of adjectives, but clear task boundaries, input formats, and acceptance criteria.\u003C\u002Fp>\u003Ch2>Practical Steps or Checklist\u003C\u002Fh2>\u003Cul>\u003Cli>\u003Cstrong>Define the task first:\u003C\u002Fstrong> Is it summarization, classification, extraction, rewriting, or code generation? Different tasks require different context and output constraints.\u003C\u002Fli>\u003Cli>\u003Cstrong>Control the context:\u003C\u002Fstrong> Do not stuff all materials into the window as-is. Prioritize content strongly relevant to the current question; when necessary, summarize, segment, or retrieve first.\u003C\u002Fli>\u003Cli>\u003Cstrong>Use structured instructions:\u003C\u002Fstrong> Clearly specify role, goal, input, constraints, and output format. For example, require the model to output only JSON, or to give the conclusion first and then the reasoning.\u003C\u002Fli>\u003Cli>\u003Cstrong>Provide few-shot examples:\u003C\u002Fstrong> If the task format is complex, provide one or two high-quality examples to reduce the model’s guessing about format.\u003C\u002Fli>\u003Cli>\u003Cstrong>Set parameters and test:\u003C\u002Fstrong> Parameters such as temperature and maximum output length affect result stability. Classification and extraction tasks usually work better with lower temperature.\u003C\u002Fli>\u003C\u002Ful>\u003Cpre>\u003Ccode># A common example of message organization\nmessages = [\n  {'role': 'system', 'content': 'You are a rigorous technical editor. Keep answers concise.'},\n  {'role': 'user', 'content': 'Explain the context window in three sentences.'}\n]\n\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp>This code shows a common message structure in conversational interfaces. Actual field names and invocation methods vary by platform; refer to official documentation for specifics.\u003C\u002Fp>\u003Ch2>Common Pitfalls and Recommendations\u003C\u002Fh2>\u003Cul>\u003Cli>\u003Cstrong>Treating the context window as permanent memory:\u003C\u002Fstrong> The model does not automatically remember history outside the window. Cross-session memory requires external storage, summarization, or retrieval systems.\u003C\u002Fli>\u003Cli>\u003Cstrong>Blindly pursuing long context:\u003C\u002Fstrong> A larger window does not necessarily mean better results. Too much irrelevant content increases cost and may make it harder for the model to grasp key points.\u003C\u002Fli>\u003Cli>\u003Cstrong>Ignoring token counting:\u003C\u002Fstrong> For long-text applications, estimate input and output tokens in advance to avoid truncation due to exceeding limits.\u003C\u002Fli>\u003Cli>\u003Cstrong>Vague prompts:\u003C\u002Fstrong> For example, writing only “help me optimize this” may leave the model unable to determine whether the optimization target is performance, readability, or style.\u003C\u002Fli>\u003Cli>\u003Cstrong>Lacking validation:\u003C\u002Fstrong> Large language models may generate content that sounds plausible but is wrong. Key fields, numbers, and code should include validation or testing.\u003C\u002Fli>\u003Cli>\u003Cstrong>Leaking sensitive information:\u003C\u002Fstrong> Do not directly include keys, private data, or internal secrets in prompts unless compliance and security boundaries have been confirmed.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>Directions for Further Reading\u003C\u002Fh2>\u003Cul>\u003Cli>\u003Cstrong>Attention mechanisms and model architecture:\u003C\u002Fstrong> Gain a deeper understanding of Query, Key, Value, multi-head attention, and positional encoding.\u003C\u002Fli>\u003Cli>\u003Cstrong>Tokenizers and token counting:\u003C\u002Fstrong> Learn how different tokenizers affect length, cost, and multilingual performance.\u003C\u002Fli>\u003Cli>\u003Cstrong>RAG (Retrieval-Augmented Generation):\u003C\u002Fstrong> When knowledge volume is large and updates frequently, combine retrieval systems to manage context.\u003C\u002Fli>\u003Cli>\u003Cstrong>Structured output and function calling:\u003C\u002Fstrong> Learn about JSON Schema, tool calling, and Agent design.\u003C\u002Fli>\u003Cli>\u003Cstrong>Evaluation and regression testing:\u003C\u002Fstrong> Establish prompt version control, example sets, and automated evaluation pipelines to make performance measurable.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Overall, large language models are not black-box magic, but probabilistic sequence models that can be constrained through engineering. Understanding how Transformers process text, the boundaries of context windows, and how to organize prompts is the first step into LLM application development.\u003C\u002Fp>\n\u003Csection class=\"portal-sources\" style=\"margin-top:2em;font-size:14px;line-height:1.7;color:#5c5850;\">\u003Cp style=\"margin:0 0 0.5em;font-weight:600;\">References\u003C\u002Fp>\u003Cul style=\"margin:0;padding-left:1.25em;\">\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F338817680\" rel=\"noopener noreferrer\" target=\"_blank\">Transformer模型详解（图解最完整版） - 知乎\u003C\u002Fa>\u003C\u002Fli>\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fblog.csdn.net\u002Fweixin_42475060\u002Farticle\u002Fdetails\u002F121101749\" rel=\"noopener noreferrer\" target=\"_blank\">【超详细】【原理篇&实战篇】一文读懂Transformer-CSDN博客\u003C\u002Fa>\u003C\u002Fli>\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fwww.runoob.com\u002Fpytorch\u002Ftransformer-model.html\" rel=\"noopener noreferrer\" target=\"_blank\">Transformer 模型 - 菜鸟教程\u003C\u002Fa>\u003C\u002Fli>\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fwww.zhihu.com\u002Ftardis\u002Fzm\u002Fart\u002F600773858\" rel=\"noopener noreferrer\" target=\"_blank\">一文了解Transformer全貌（图解Transformer）\u003C\u002Fa>\u003C\u002Fli>\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F675569052\" rel=\"noopener noreferrer\" target=\"_blank\">Transformer原理详解（图解完整版附代码） - 知乎\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Ful>\u003C\u002Fsection>\n\u003Csection class=\"portal-disclaimer\" data-portal-disclaimer=\"1\" style=\"margin-top:2.5em;padding-top:1.25em;border-top:1px solid #e8e4dc;font-size:14px;line-height:1.7;color:#7a756c;\">\u003Cp style=\"margin:0;\">Disclaimer: This article is compiled from publicly available online sources for educational purposes only and does not represent the official position of Guanshan Academy. If you believe your rights have been infringed, please contact us for removal.\u003C\u002Fp>\u003Cp style=\"margin:0.75em 0 0;\">Contact: \u003Ca href=\"mailto:chenxj.g@gmail.com\">chenxj.g@gmail.com\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fsection>","2026-09-29T12:29:55.757097+08:00","2026-09-29T12:32:34.448088+08:00","2026-09-29T21:35:01.849273+08:00",{"id":108,"locale":13,"slug":109,"type":15,"title":110,"summary":111,"content_html":112,"video_url":19,"cover_url":54,"author":75,"category":22,"status":23,"published_at":113,"created_at":114,"updated_at":115},154,"大语言模型入门-从-transformer-到上下文窗口与提示词","Getting Started with Large Language Models: From Transformers to Context Windows and Prompts","This article outlines three foundational concepts for developers: how Transformers use attention to process text, how context windows constrain the information a model can see, and how prompt engineering improves output stability through structured input. It also provides a practical checklist and common pitfalls.","\u003Ch2>Background and Problems\u003C\u002Fh2>\u003Cp>When developers first encounter large language models, they often treat them as a smarter text completion API. However, when applying them to question answering, summarization, code generation, or enterprise knowledge bases, three common issues arise: Why does the model give irrelevant answers? Why is information in the latter part of a long document ignored? Why can the same question produce very different results when phrased differently?\u003C\u002Fp>\n\u003Cfigure class=\"portal-article-figure\">\u003Cimg src=\"https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Flandscape_mountain.jpg\" alt=\"观山静思\" loading=\"lazy\" decoding=\"async\" \u002F>\u003Cfigcaption>观山静思\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u003Cp>These questions usually come back to three foundational concepts: Transformer, context window, and prompt. The Transformer determines how a model understands and generates text; the context window determines how much content the model can see at once; the prompt determines what information you provide to the model and in what structure. Understanding these three concepts is the first step from calling an API to building stable AI applications.\u003C\u002Fp>\u003Ch2>Core Concepts\u003C\u002Fh2>\u003Ch3>Transformer: Understanding Sequences with Attention\u003C\u002Fh3>\u003Cp>According to public sources, the Transformer was proposed by Vaswani and others in the 2017 paper Attention Is All You Need. Unlike traditional RNNs that process words one by one, the core of a Transformer is self-attention: when processing a word, the model can simultaneously attend to information from other positions in the sentence and calculate the strength of their relationships.\u003C\u002Fp>\u003Cp>An engineering analogy may help: if a sentence is viewed as a set of service calls, self-attention is like dynamically querying the relevance of other nodes each time a node is processed, then aggregating the results by weight. A typical pipeline includes converting text into word vectors, adding positional encodings, computing Query, Key, and Value, obtaining attention scores through scaled dot products, and aggregating them into a new representation.\u003C\u002Fp>\u003Cp>Modern large language models commonly use Encoder, Decoder, or Decoder-only architectures. According to public sources, BERT-style models lean toward Encoder representations, while GPT-style generative models usually use a Decoder-only architecture. Application developers do not need to implement a model from scratch, but they should know that models do not retrieve text word by word; they predict based on contextual probability distributions.\u003C\u002Fp>\u003Ch3>Context Window: The Range of Tokens a Model Can See at Once\u003C\u002Fh3>\u003Cp>A context window usually refers to the maximum number of tokens a model can process in a single inference. A token does not necessarily correspond to a Chinese character or an English word; it may be split into subwords or character fragments by the tokenizer. A larger window allows the model to reference more history at the same time, but it does not mean unlimited memory.\u003C\u002Fp>\u003Cp>Context windows introduce three engineering constraints:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Capacity limits\u003C\u002Fstrong>: The input, system prompt, conversation history, and reserved output all consume tokens.\u003C\u002Fli>\u003Cli>\u003Cstrong>Attention cost\u003C\u002Fstrong>: Longer windows usually increase inference latency and memory usage.\u003C\u002Fli>\u003Cli>\u003Cstrong>Information decay\u003C\u002Fstrong>: Even when the window is long enough, the model may pay insufficient attention to middle or later content, so structured formatting is needed.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Therefore, long-text applications should not simply stuff all materials into the prompt. Instead, use summarization, retrieval, chunking, and ranking.\u003C\u002Fp>\u003Ch3>Prompts: Turning Tasks into an Executable Input Protocol\u003C\u002Fh3>\u003Cp>A prompt is not a magic spell; it is more like API documentation. A good prompt should clearly define the task objective, input materials, output format, constraints, and examples. For complex tasks, it can also include role setting, reasoning-step requirements, or refusal policies.\u003C\u002Fp>\u003Cp>For example, when asking a model to summarize a technical document, instead of writing Help me summarize it, provide structured fields such as background, solution, risks, and conclusion. This makes the output more stable and easier for programs to parse.\u003C\u002Fp>\u003Ch2>Practical Steps or Checklist\u003C\u002Fh2>\u003Cul>\u003Cli>\u003Cstrong>Step 1: Define the task type\u003C\u002Fstrong>. Determine whether it is classification, extraction, summarization, rewriting, question answering, or code generation. The clearer the task, the easier it is to design the prompt.\u003C\u002Fli>\u003Cli>\u003Cstrong>Step 2: Estimate the context budget\u003C\u002Fstrong>. Reserve tokens for the system prompt, user input, reference materials, and output to avoid exceeding the limit.\u003C\u002Fli>\u003Cli>\u003Cstrong>Step 3: Organize the input structure\u003C\u002Fstrong>. Use headings, numbering, lists, and separators, and place key information in prominent positions.\u003C\u002Fli>\u003Cli>\u003Cstrong>Step 4: Provide minimal but sufficient examples\u003C\u002Fstrong>. If the output format is strict, one to three examples are often more effective than lengthy explanations.\u003C\u002Fli>\u003Cli>\u003Cstrong>Step 5: Ask the model to indicate uncertainty\u003C\u002Fstrong>. For example, ask it to state what information is missing if the materials are insufficient. This reduces hallucination risk.\u003C\u002Fli>\u003Cli>\u003Cstrong>Step 6: Run regression tests\u003C\u002Fstrong>. Prepare a set of typical questions, edge cases, and abnormal inputs, and continuously compare different prompt versions.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>A simple example is to construct a list of conversation messages, separating the system instruction, user question, and context materials:\u003C\u002Fp>\u003Cpre>\u003Ccode>messages = [{'role': 'system', 'content': 'You are a technical documentation editor and answer only based on the provided materials'}, {'role': 'user', 'content': 'Materials: ... Question: What is a context window?'}]\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp>This code is not a complete API call; it illustrates that in engineering practice, roles, rules, and questions should be passed in layers rather than mixed into a single block of natural language.\u003C\u002Fp>\u003Ch2>Common Pitfalls and Suggestions\u003C\u002Fh2>\u003Cul>\u003Cli>\u003Cstrong>Treating a long window as unlimited memory\u003C\u002Fstrong>. A larger window only means more tokens can be input; it does not mean every detail will be used equally. For long documents, add a table of contents, summaries, and retrieval augmentation.\u003C\u002Fli>\u003Cli>\u003Cstrong>Over-relying on the model for prompt compliance\u003C\u002Fstrong>. If JSON output is required, specify field names, types, and null handling, and validate with post-processing when necessary.\u003C\u002Fli>\u003Cli>\u003Cstrong>Ignoring token-counting differences\u003C\u002Fstrong>. Chinese, English, code, and punctuation are tokenized differently. Do not estimate by character count alone; follow the tokenizer or official documentation of the model you use.\u003C\u002Fli>\u003Cli>\u003Cstrong>Frequently changing prompts without an evaluation set\u003C\u002Fstrong>. Without a fixed test set, it is hard to know whether an optimization is a real improvement or random fluctuation.\u003C\u002Fli>\u003Cli>\u003Cstrong>Treating model output as a source of truth\u003C\u002Fstrong>. Large language models may generate plausible but incorrect content. In critical scenarios, combine them with authoritative data, source citations, or human review.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Adopt a prompts as code approach: version control, comments, A\u002FB testing, and turning failure cases into regression tests.\u003C\u002Fp>\u003Ch2>Further Reading Directions\u003C\u002Fh2>\u003Cul>\u003Cli>The original Transformer paper Attention Is All You Need and illustrated explanations can help you understand self-attention, multi-head attention, and positional encoding.\u003C\u002Fli>\u003Cli>Tokenizer and token-counting tools help you understand context windows and estimate costs.\u003C\u002Fli>\u003Cli>RAG, or retrieval-augmented generation, and vector databases are suitable for long documents, enterprise knowledge bases, and real-time data.\u003C\u002Fli>\u003Cli>Prompt evaluation and automated optimization, such as fixed datasets, metric scoring, and prompt version management.\u003C\u002Fli>\u003Cli>Basics of model safety and alignment, including prompt injection, sensitive information leakage, and output content moderation.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Overall, building large language model applications is not magic. If you understand how Transformers model sequences, respect the engineering limits of context windows, and use structured prompts to define tasks clearly, output quality usually improves significantly. For developers, the stronger these foundations are, the fewer pitfalls they will encounter when building Agents, RAG, and automated workflows.\u003C\u002Fp>\n\u003Csection class=\"portal-sources\" style=\"margin-top:2em;font-size:14px;line-height:1.7;color:#5c5850;\">\u003Cp style=\"margin:0 0 0.5em;font-weight:600;\">References\u003C\u002Fp>\u003Cul style=\"margin:0;padding-left:1.25em;\">\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F338817680\" rel=\"noopener noreferrer\" target=\"_blank\">Transformer模型详解（图解最完整版） - 知乎\u003C\u002Fa>\u003C\u002Fli>\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fblog.csdn.net\u002Fweixin_42475060\u002Farticle\u002Fdetails\u002F121101749\" rel=\"noopener noreferrer\" target=\"_blank\">【超详细】【原理篇&实战篇】一文读懂Transformer-CSDN博客\u003C\u002Fa>\u003C\u002Fli>\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fwww.runoob.com\u002Fpytorch\u002Ftransformer-model.html\" rel=\"noopener noreferrer\" target=\"_blank\">Transformer 模型 - 菜鸟教程\u003C\u002Fa>\u003C\u002Fli>\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fwww.zhihu.com\u002Ftardis\u002Fzm\u002Fart\u002F600773858\" rel=\"noopener noreferrer\" target=\"_blank\">一文了解Transformer全貌（图解Transformer）\u003C\u002Fa>\u003C\u002Fli>\u003Cli style=\"margin:0.25em 0;\">\u003Ca href=\"https:\u002F\u002Fzhuanlan.zhihu.com\u002Fp\u002F675569052\" rel=\"noopener noreferrer\" target=\"_blank\">Transformer原理详解（图解完整版附代码） - 知乎\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Ful>\u003C\u002Fsection>\n\u003Csection class=\"portal-disclaimer\" data-portal-disclaimer=\"1\" style=\"margin-top:2.5em;padding-top:1.25em;border-top:1px solid #e8e4dc;font-size:14px;line-height:1.7;color:#7a756c;\">\u003Cp style=\"margin:0;\">Disclaimer: This article is compiled from publicly available online sources for educational purposes only and does not represent the official position of Guanshan Academy. If you believe your rights have been infringed, please contact us for removal.\u003C\u002Fp>\u003Cp style=\"margin:0.75em 0 0;\">Contact: \u003Ca href=\"mailto:chenxj.g@gmail.com\">chenxj.g@gmail.com\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fsection>","2026-09-29T12:22:23.745054+08:00","2026-09-29T12:25:25.031152+08:00","2026-09-29T21:35:06.414152+08:00",{"id":117,"locale":13,"slug":118,"type":15,"title":119,"summary":120,"content_html":121,"video_url":19,"cover_url":54,"author":21,"category":22,"status":23,"source_job_id":122,"published_at":123,"created_at":124,"updated_at":125},158,"ai-agent-落地实战-搭可控闭环-三天跑通低风险场景","AI Agents in Practice: Build a Controllable Closed Loop and Get a Low-Risk Scenario Running in Three Days","By the end, you'll have a task closed-loop table and a rollback checklist—pick a low-risk scenario right away and complete an Agent pilot validation in three days.","\u003Csection data-gzh-readable=\"1\" style=\"margin:0;padding:0;\">\u003Cdiv data-gzh-header=\"1\">\u003Cp style=\"font-size:15px;color:#888;text-align:center;margin-top:8px;\">By the end, you'll have a task closed-loop table and a rollback checklist—pick a low-risk scenario right away and complete an Agent pilot validation in three days.\u003C\u002Fp>\n\u003Cfigure class=\"portal-article-figure\">\u003Cimg src=\"https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Flandscape_mountain.jpg\" alt=\"观山静思\" loading=\"lazy\" decoding=\"async\" \u002F>\u003Cfigcaption>观山静思\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u003Cp style=\"font-size:14px;color:#999;text-align:center;margin-top:4px;\">Author: Jinxuezhai · Guanshan Academy\u003C\u002Fp>\u003Cp style=\"font-size:13px;color:#999;text-align:center;margin:0 0 6px 0;\">About 3,680 words · Approx. 13-minute read\u003C\u002Fp>\u003Chr style=\"border:none;border-top:1px solid #eee;margin:24px 0;\" \u002F>\u003C\u002Fdiv>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Recently, many teams have been moving \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">Agent\u003C\u002Fcode> from demos into real business: the chat window can draft proposals, look up information, and answer process questions—but the moment it enters a real system, it gets stuck on permissions, rollback, and auditing. According to public information and industry observation, most pilots fail not because the Agent can't answer, but because it can't bring things to a clean close. This article gives you a controllable closed-loop framework: a task scenario table, a pipeline breakdown, and a go-live checklist—by the end, you can immediately pick a low-risk scenario for a three-day pilot.\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">1. Choose the Task First: Start with High-Frequency, Low-Loss, Verifiable Work\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">When putting an \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">Agent\u003C\u002Fcode> into production, don't make your first cut at the big workflows. Prioritize tasks with clear inputs and outputs and low failure costs, such as organizing materials, generating drafts, and classifying tickets. You can experiment with these first: if the answer is wrong, at worst you start over; if it's right, you can see the time saved directly.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Conversely, hold off on delegating actions like automatic price changes, automatic publishing, and automatic deletion. These often touch external state directly, and once a misjudgment happens, the cost of cleaning up escalates quickly.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">When selecting a scenario, filter with three questions: Can it leave an audit trail? Can a human step in? Can it be rolled back? If any one is missing, don't delegate yet. An audit trail makes the process reviewable, human intervention provides a safety net for exceptions, and rollback makes errors closable. Without these three things, so-called automation only pushes risk further away.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Write your goals as acceptance criteria. Don't just talk about efficiency gains—track the manual review rate, average handling time, number of anomalous tasks, and result adoption rate. Run a baseline first, then talk optimization. Metrics shouldn't come from gut feeling; they turn the pilot into a testable judgment.\u003C\u002Fp>\u003Ctable style=\"width:100%;border-collapse:collapse;font-size:13px;margin:16px 0;\">\u003Ctr>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Task Type\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Suitable Entry Point\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Unsuitable Entry Point\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Recommended Action\u003C\u002Fth>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Material organization\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Extracting key points, categorizing tags, generating summaries\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Writing directly to the production database and bulk publishing\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Read-only retrieval, with results going to a staging area first\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Draft generation\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Providing first drafts for copy, replies, and proposals\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Confirming outbound sends on someone's behalf\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Generate drafts and wait for human editor confirmation\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Ticket classification\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Identifying type, priority, and suggested routing\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Automatically closing customer tickets\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Suggestions only, keeping a manual switch\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Data processing\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Field mapping, missing-value alerts, anomaly flags\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Directly modifying live business tables\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Produce a diff report, execute after human approval\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Transaction operations\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Querying orders, explaining failure reasons\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Automatic refunds, price changes, and publishing\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Read-only queries; write actions go through the approval chain\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftable>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Three Steps You Can Take Right Now\u003C\u002Fh3>\u003Col>\u003Cli>List your repetitive tasks from the past two weeks, noting inputs, outputs, and error costs.\u003C\u002Fli>\u003Cli>Use the three questions—audit trail, human intervention, rollback—to narrow down to 1 candidate scenario.\u003C\u002Fli>\u003Cli>Establish a baseline for that scenario: record current manual time spent, review ratio, and common errors.\u003C\u002Fli>\u003C\u002Fol>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">\u003Cem>—— A clear pipeline is what makes the closed loop stable.\u003C\u002Fem>\u003C\u002Fp>\u003Cp style=\"text-align:center;margin:16px 0 6px 0;\">\u003Cimg src=\"http:\u002F\u002Fmmbiz.qpic.cn\u002Fsz_mmbiz_jpg\u002F4WyfSFibfkRNElAlj8WTYjibd1II3KfDNDh8H3QzQ8RH8YQGC0kXJGUEicZFy6V5fSB6yeOMySabW6GCDibdG5v7pXFez8KQdrgGukCiaTHWAx3c\u002F0?from=appmsg\" alt=\"Image · Layered distant mountains\" style=\"max-width:100%;border-radius:6px;display:block;margin:0 auto;\" \u002F>\u003C\u002Fp>\u003Cp style=\"text-align:center;font-size:12px;color:#bbb;margin:0 0 18px 0;\">Image · Layered distant mountains\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">2. Break Down the Pipeline: Turn the Agent into Events, Tools, and State\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Many \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">Agent\u003C\u002Fcode> pilots fail not because the model can't think, but because there's no engineering pipeline. You can't treat a chat window as a system. A truly deployable \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">Agent\u003C\u002Fcode> must be broken down into \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">events\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">tools\u003C\u002Fcode>, and \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">state\u003C\u002Fcode>: who triggers it, what it plans, which tools it calls, where results are stored, and how humans see them.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">First sketch the minimal pipeline: \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">event trigger\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">model planning\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">tool invocation\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">state write\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">result return\u003C\u002Fcode>. Every node must be observable. If a step can only be explained by the \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">Prompt\u003C\u002Fcode> rather than captured in logs, state tables, or audit fields, troubleshooting later will be painful.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Steps to Break Down the Minimal Pipeline\u003C\u002Fh3>\u003Col>\u003Cli>Define the event: change the task entry point from user chat to \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">task.created\u003C\u002Fcode>.\u003C\u002Fli>\u003Cli>Define the goal: turn vague instructions into verifiable deliverables, such as summaries, classifications, drafts, or recommendations.\u003C\u002Fli>\u003Cli>Define the tools: separate \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">read-only tools\u003C\u002Fcode> from \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">write tools\u003C\u002Fcode>, and get the read-only tools working first.\u003C\u002Fli>\u003Cli>Define the state: for each task, record the current step, inputs, outputs, elapsed time, and error codes.\u003C\u002Fli>\u003Cli>Define the return: results don't directly overwrite production; they go into a staging or approval area first.\u003C\u002Fli>\u003C\u002Fol>\u003Cpre style=\"background:#f5f7fa;padding:15px;border-radius:6px;font-size:14px;line-height:1.6;overflow-x:auto;margin:1em 0;\">\u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">task_id = create_event(source, payload)\nplan = agent.plan(goal=task.goal, context=task.state)\nread_output = tool.search(query=plan.query)\ndraft = tool.generate(content=read_output)\nstate.save(task_id, status=draft_ready, snapshot=read_output)\nhuman.review(draft)\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">\u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">Read-only tools\u003C\u002Fcode> and \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">write tools\u003C\u002Fcode> must be kept separate. Queries, retrieval, summarization, and format conversion can be used right away; placing orders, publishing, deleting, refunds, and price changes must first go through the approval chain. If this boundary isn't clearly written down, the smarter the \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">Agent\u003C\u002Fcode>, the more easily small mistakes turn into incidents.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Long-running tasks need to save \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">intermediate state\u003C\u002Fcode>. For example, retrieval completes but generation times out; if the state doesn't record this, a retry has to run from scratch, wasting tool calls and easily producing duplicate results. You can leave checkpoints at key steps: completed, resumable, needs human confirmation.\u003C\u002Fp>\u003C\u002Fsection>\n\u003Csection class=\"portal-disclaimer\" data-portal-disclaimer=\"1\" style=\"margin-top:2.5em;padding-top:1.25em;border-top:1px solid #e8e4dc;font-size:14px;line-height:1.7;color:#7a756c;\">\u003Cp style=\"margin:0;\">Disclaimer: This article is compiled from publicly available online sources for educational purposes only and does not represent the official position of Guanshan Academy. If you believe your rights have been infringed, please contact us for removal.\u003C\u002Fp>\u003Cp style=\"margin:0.75em 0 0;\">Contact: \u003Ca href=\"mailto:chenxj.g@gmail.com\">chenxj.g@gmail.com\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fsection>",3491398,"2026-09-29T12:19:36+08:00","2026-09-29T12:36:55.394477+08:00","2026-09-29T21:35:01.860963+08:00",{"id":127,"locale":13,"slug":128,"type":15,"title":129,"summary":130,"content_html":131,"video_url":19,"cover_url":20,"author":21,"category":22,"status":23,"source_job_id":132,"published_at":133,"created_at":134,"updated_at":135},160,"agent-落地实战-把失败写成可恢复状态","Agent in Production: Turning Failures into Recoverable States","After reading, you get a business event-sharding table, a failure-recovery checklist, and pre-launch validation steps to complete a controlled Agent pilot in two days.","\u003Csection data-gzh-readable=\"1\" style=\"margin:0;padding:0;\">\u003Cdiv data-gzh-header=\"1\">\u003Cp style=\"font-size:15px;color:#888;text-align:center;margin-top:8px;\">After reading, you get a business event-sharding table, a failure-recovery checklist, and pre-launch validation steps to complete a controlled Agent pilot in two days.\u003C\u002Fp>\n\u003Cfigure class=\"portal-article-figure\">\u003Cimg src=\"https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Freading_books.jpg\" alt=\"读书明理\" loading=\"lazy\" decoding=\"async\" \u002F>\u003Cfigcaption>读书明理\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u003Cp style=\"font-size:14px;color:#999;text-align:center;margin-top:4px;\">Author: Jinxue Studio · Guanshan Academy\u003C\u002Fp>\u003Cp style=\"font-size:13px;color:#999;text-align:center;margin:0 0 6px 0;\">About 3,789 characters · about 13 minutes to read\u003C\u002Fp>\u003Chr style=\"border:none;border-top:1px solid #eee;margin:24px 0;\" \u002F>\u003C\u002Fdiv>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">This article gives you an event-sharding table, a failure-recovery checklist, and pre-launch inspection steps. You can use them to run a low-risk Agent pilot: it can look up materials, generate drafts, and record state; when it fails, you know who takes over, how to recover, and what evidence supports the review.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Recently, many teams have turned Agents into “conference-room stars”: they can read tickets, search knowledge bases, and write drafts; in a demo, one sentence can pull the materials together. But once they are connected to real business processes, the problems appear: sequential calls need state confirmation, failures need retries, write operations need traces, and audits need traceability.\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">I. Background: Why Demos Stop in the Conference Room\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Q&A, retrieval, and drafting are short tasks with light failures, so they are good for demos. Business processes need continuous execution: first query the ticket, then read the knowledge base, then generate a reply draft, then write the result into the system.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Once the chain gets long, failure is no longer “the result is inaccurate,” but “whether it has already run,” “whether it ran twice,” and “whether it can be undone.” Based on public materials and industry observation, many pilots settle quickly on model selection but are slow to fill in event sharding, state recording, and rollback actions.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">You can first choose a low-risk closed loop: internal ticket summarization, log classification, or customer-service knowledge retrieval. The key is not that the task is simple, but that failure will not contaminate production data.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">— Being able to run is only the starting point; being recoverable is what earns production eligibility.\u003C\u002Fp>\u003Cp style=\"text-align:center;margin:16px 0 6px 0;\">\u003Cimg src=\"http:\u002F\u002Fmmbiz.qpic.cn\u002Fsz_mmbiz_jpg\u002F4WyfSFibfkRPEsnGZzO4vcZZgrgUYbxicOtvtrT7HInqmLrqxBlV1M3bQZEicHjzAyR2IgaszGn3ibX5cz9S5tbzASwuax6ppLRG4JvhiaATWP9Y\u002F0?from=appmsg\" alt=\"Illustration · Soft Light in Spring Fields\" style=\"max-width:100%;border-radius:6px;display:block;margin:0 auto;\" \u002F>\u003C\u002Fp>\u003Cp style=\"text-align:center;font-size:12px;color:#bbb;margin:0 0 18px 0;\">Illustration · Soft Light in Spring Fields\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">II. Mechanism: State Sharding and Runtime Evidence\u003C\u002Fh2>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">1. Cut Long Workflows into Stoppable, Auditable Steps\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">A runnable Agent should not be treated as a one-shot black box. A more stable approach is to split the workflow into short nodes such as event, decision, invocation, validation, and delivery. Each node needs inputs, outputs, timeouts, retries, and human takeover points.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">For example, “customer-service ticket summarization” can be split into: read ticket, search knowledge base, generate draft, check sensitive fields, save draft, and notify a human. Each step has its own state, so when it fails you know where it is stuck.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">2. Leave a Minimal Evidence Package for Each Node\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The evidence package is not a chat log, nor free-form text. It must answer: who, at which step, which tool was used, what action was taken, and whether it continued automatically.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The minimal fields can be few: \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">task_id\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">event_id\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">step_id\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">input_digest\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">tool_name\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">params_masked\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">result_status\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">duration_ms\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">next_action\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">retryable\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">human_required\u003C\u002Fcode>, \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">rollback_basis\u003C\u002Fcode>.\u003C\u002Fp>\u003Cpre style=\"background:#f5f7fa;padding:15px;border-radius:6px;font-size:14px;line-height:1.6;overflow-x:auto;margin:1em 0;\">\u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">{\n  "task_id": "ticket-042",\n  "event_id": "cust-export-timeout",\n  "step_id": "summarize-draft",\n  "input_digest": "Customer report: export report timed out",\n  "tool_name": "kb.search",\n  "params_masked": "tenant=t-a1b2|query=export",\n  "result_status": "success",\n  "duration_ms": 1800,\n  "next_action": "save_draft",\n  "retryable": false,\n  "human_required": false,\n  "rollback_basis": "no_write"\n}\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">This record does not aim to be elegant; it aims to withstand scrutiny. When auditors see the status as \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">success\u003C\u002Fcode>, they also need to see whether the next step is \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">save_draft\u003C\u002Fcode>; when they see a timeout as \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">timeout\u003C\u002Fcode>, they also need to know whether it entered human confirmation.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">3. Do Not Rely on Free-Form Text for State\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Descriptions such as “draft generated” feel natural, but they can become unreliable during audits. More reliable state fields are: whether it has executed, whether it is retryable, whether human confirmation is required, and whether there is a rollback basis.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">If a write operation has been sent but the external system times out, you cannot simply mark it as failed, nor automatically resend it. It needs at least three markers: “may have occurred,” “requires human confirmation,” and “compensation action available.”\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">— Engineering is not about making machines talk better; it is about making every step withstand scrutiny.\u003C\u002Fp>\u003Cp style=\"text-align:center;margin:16px 0 6px 0;\">\u003Cimg src=\"http:\u002F\u002Fmmbiz.qpic.cn\u002Fmmbiz_jpg\u002F4WyfSFibfkRN0bZC7VuyXtxwZJiaykyZUcUovfqyfLshkRXiaA7yEYMteC3VReWzdeVxcNSVOXSVAIqkJNoqKIBLXxrEtWEFN7K8NTqdd63dbk\u002F0?from=appmsg\" alt=\"Illustration · Garden Path\" style=\"max-width:100%;border-radius:6px;display:block;margin:0 auto;\" \u002F>\u003C\u002Fp>\u003Cp style=\"text-align:center;font-size:12px;color:#bbb;margin:0 0 18px 0;\">Illustration · Garden Path\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">III. Steps: Run a Small Closed Loop in Two Days\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">If the schedule is tight, you can fold the Day 3 checks into the end of Day 2. The core is to make the evidence complete first, then make the workflow run smoothly.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Day1: Establish Admission and Permissions\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Choose a low-risk event, such as internal ticket summarization. First write a task admission table: what events can enter, which fields must be masked, and which results must not be sent automatically.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Then write a tool permission table: allow only read-only queries, summary generation, and draft saving. Do not give the Agent actions such as production database writes, message sending, or refund compensation at first.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Event-Sharding Table\u003C\u002Fh3>\u003Ctable style=\"width:100%;border-collapse:collapse;font-size:13px;margin:16px 0;\">\u003Ctr>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Node\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Input\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Output\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Timeout\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Failure action\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Human checkpoint\u003C\u002Fth>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Read ticket\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Ticket ID, tenant identifier\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Problem summary, field list\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">API contract\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Skip and notify a human\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Confirm when fields are missing\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Search knowledge base\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Summary keywords\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">\n\u003Csection class=\"portal-disclaimer\" data-portal-disclaimer=\"1\" style=\"margin-top:2.5em;padding-top:1.25em;border-top:1px solid #e8e4dc;font-size:14px;line-height:1.7;color:#7a756c;\">\u003Cp style=\"margin:0;\">Disclaimer: This article is compiled from publicly available online sources for educational purposes only and does not represent the official position of Guanshan Academy. If you believe your rights have been infringed, please contact us for removal.\u003C\u002Fp>\u003Cp style=\"margin:0.75em 0 0;\">Contact: \u003Ca href=\"mailto:chenxj.g@gmail.com\">chenxj.g@gmail.com\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fsection>",3491397,"2026-09-29T12:15:42+08:00","2026-09-29T12:54:47.846895+08:00","2026-09-29T21:35:01.865938+08:00",{"id":137,"locale":13,"slug":138,"type":15,"title":139,"summary":140,"content_html":141,"video_url":19,"cover_url":20,"author":21,"category":22,"status":23,"source_job_id":142,"published_at":143,"created_at":144,"updated_at":145},162,"agent试点启动包-三表七步验收法","Agent Pilot Starter Kit: A Three-Table, Seven-Step Acceptance Method","After reading, you can get the task admission table, permission boundary table, and acceptance table, and use a seven-day pilot to determine which operations can be automated, require human approval, or must be disabled.","\u003Csection data-gzh-readable=\"1\" style=\"margin:0;padding:0;\">\u003Cdiv data-gzh-header=\"1\">\u003Cp style=\"font-size:15px;color:#888;text-align:center;margin-top:8px;\">After reading, you can get the task admission table, permission boundary table, and acceptance table, and use a seven-day pilot to determine which operations can be automated, require human approval, or must be disabled.\u003C\u002Fp>\n\u003Cfigure class=\"portal-article-figure\">\u003Cimg src=\"https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Freading_books.jpg\" alt=\"读书明理\" loading=\"lazy\" decoding=\"async\" \u002F>\u003Cfigcaption>读书明理\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u003Cp style=\"font-size:14px;color:#999;text-align:center;margin-top:4px;\">Author: Jinxue Studio · Guanshan Academy\u003C\u002Fp>\u003Cp style=\"font-size:13px;color:#999;text-align:center;margin:0 0 6px 0;\">Full text: approx. 3,261 characters · About 11-minute read\u003C\u002Fp>\u003Chr style=\"border:none;border-top:1px solid #eee;margin:24px 0;\" \u002F>\u003C\u002Fdiv>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Recently, in discussions about AI agents, you often hear: models can write, retrieve, and call tools. But once they receive real tickets, problems appear immediately: Who authorizes them to change orders? How do you roll back a wrong answer? Which steps can only generate drafts?\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">This article does not talk about grand architecture. It only provides a pilot starter kit: three tables to close the scope of scenarios, seven days and seven steps to run through acceptance, so that business, technology, and operations can judge on the same basis what can be automated today and where human control must be enforced.\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">1. First decide whether to do it: three gaps in Agent pilots\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Many teams start projects from model capabilities and first look for the most impressive tasks. A more stable approach is to reverse this: first look for business gaps. Gaps usually fall into three categories: slow information organization, heavy cross-system data movement, and rule-based judgments that are easy to miss. If an Agent only makes answers more fluent without changing process cost, it is not worth doing.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">1. First choose high-frequency, low-damage, and verifiable tasks\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Pilot tasks should ideally meet three conditions: high occurrence frequency, limited impact when errors occur, and results that can be judged with samples. Ticket summaries, document classification, and draft generation are suitable to start with; payment refunds, bulk price changes, and contract stamping are not suitable for the first batch.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">You can use one sentence to filter: if this happens every day and a single error can be remedied quickly, it is suitable for a read-only pilot.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">2. Every task must have inputs, outputs, and success conditions\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Many projects get stuck because they can chat but cannot deliver, due to tasks lacking boundaries. You must clearly specify what the Agent inputs, outputs, what counts as passing, and where it falls back on failure.\u003C\u002Fp>\u003Cpre style=\"background:#f5f7fa;padding:15px;border-radius:6px;font-size:14px;line-height:1.6;overflow-x:auto;margin:1em 0;\">\u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">Task example: first-screen summary of a customer ticket. Input = ticket number | user's original words | attachment list | historical interaction records; Output = three-line summary | to-do list | candidate risk-level values; Success = summary covers key demands, to-dos can be routed to the designated group, risk level can be counted; Failure = retain the original text, mark it as requiring manual initial screening, and do not generate an automatic reply.\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">If you cannot write a fallback for failure, the scenario is still too vague. Do not connect tools yet.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">3. The first batch defaults to read-only or draft mode\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Let the Agent produce candidates instead of directly changing live data. Read-only mode can see context, and draft mode can generate results that humans can edit. These two levels are suitable for building trust and also keep an entry point for accountability.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">\u003Cem>—— First close the scope, then grant authority\u003C\u002Fem>\u003C\u002Fp>\u003Cp style=\"text-align:center;margin:16px 0 6px 0;\">\u003Cimg src=\"http:\u002F\u002Fmmbiz.qpic.cn\u002Fmmbiz_jpg\u002F4WyfSFibfkRMjDYF1zHTrAwUTtmibxnT52CQElosaibPV1d8yjTQxuxGlficDuoFMhrRtKPcrmZCJSCLERLlR5c4j4D6qPZJJx32wTZWIVRqBW8\u002F0?from=appmsg\" alt=\"配图·湖光暮色\" style=\"max-width:100%;border-radius:6px;display:block;margin:0 auto;\" \u002F>\u003C\u002Fp>\u003Cp style=\"text-align:center;font-size:12px;color:#bbb;margin:0 0 18px 0;\">Illustration: Lake light at dusk\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">2. Three project initiation tables: turn scenarios into acceptable pilots\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">A pilot is not just opening a chat window. Before implementation, turn the scenario into three tables: the admission table decides what to do, the permission table decides what can be touched, and the acceptance table decides what counts as passing.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">1. Task admission table\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">This table is suitable for business owners to fill out. The goal is to avoid trying to do everything.\u003C\u002Fp>\u003Cul>\u003Cli>Task name: for example, after-sales ticket assignment suggestion.\u003C\u002Fli>\u003Cli>Frequency: high, medium, low. Only high frequency can easily create scale benefits.\u003C\u002Fli>\u003Cli>Impact scope: does it only affect internal processes, or does it touch customers, funds, and permissions?\u003C\u002Fli>\u003Cli>Rollback capability: can it be quickly remedied through drafts, cancellation, or manual override?\u003C\u002Fli>\u003Cli>Owner: who judges quality and who handles exceptions?\u003C\u002Fli>\u003C\u002Ful>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">2. Permission boundary table\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">This table is maintained jointly by technology and security. The Agent cannot receive a universal key; instead, permissions must be graded by tool, field, and action.\u003C\u002Fp>\u003Cpre style=\"background:#f5f7fa;padding:15px;border-radius:6px;font-size:14px;line-height:1.6;overflow-x:auto;margin:1em 0;\">\u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">Permission example: order after-sales scenario. Tools\u002FAPIs = order query interface | ticket creation interface | user notification interface; Read\u002Fwrite levels = order query read-only | ticket creation draft | user notification requires approval; Sensitive fields = phone number | address | refund reason | invoice information, masked by default; Approvers = after-sales supervisor or on-duty operations; Disabled items = automatic refunds | bulk price changes | deletion of original tickets | cross-tenant reads.\u003C\u002Fcode>\u003C\u002Fpre>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The principle is: read is broader than write, draft is broader than execution, and sensitive actions must have approvers and audit trails.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">3. Acceptance table\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The acceptance table divides results into three levels to avoid the launch meeting ending with only \"looks good\".\u003C\u002Fp>\u003Ctable style=\"width:100%;border-collapse:collapse;font-size:13px;margin:16px 0;\">\u003Cthead>\u003Ctr>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Level\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Trigger condition\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Handling action\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Sample requirement\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Error tolerance\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Auto pass\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Input complete, format validation passed, risk fields empty or low risk\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Write into candidate pool and enter manual queue\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">First batch covers normal samples and boundary samples\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Only low-risk format or hint-type errors allowed\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Manual review\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Involves sensitive fields, conflicts with historical conclusions, abnormal tool returns, insufficient basis for judgment\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Pause automatic execution and submit to approver for confirmation\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Sample review by category\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Misjudgments acceptable, but reasons must be recorded\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Directly disabled\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Cross-system writes, fund handling, customer commitments, compliance clause generation\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Only generate explanatory text, do not execute actions\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Build a disabled list and review regularly\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Zero tolerance; any breach triggers a circuit breaker\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Do not guess the sample size. According to public materials and industry practice, the first batch can draw one batch each of normal samples, historical failure samples, and boundary samples; only after you can form reproducible pass rates, misjudgment types, and manual time costs should you talk about scaling.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">\u003Cem>—— Seven days is not a sprint; it is calibration\u003C\u002Fem>\u003C\u002Fp>\u003Cp style=\"text-align:center;margin:16px 0 6px 0;\">\u003Cimg src=\"http:\u002F\u002Fmmbiz.qpic.cn\u002Fmmbiz_jpg\u002F4WyfSFibfkRORCc3Mr3XXxnwe1YVVazFTcTn5yAfTVZEBx2OCXJklf6I4gsicvjvKiccorvRGJ513iaPUuX63mQ91niav2Cwtyt2xUvZnYeVBMqE\u002F0?from=appmsg\" alt=\"配图·晨雾山色\" style=\"max-width:100%;border-radius:6px;display:block;margin:0 auto;\" \u002F>\u003C\u002Fp>\u003Cp style=\"text-align:center;font-size:12px;color:#bbb;margin:0 0 18px 0;\">Illustration: Mountain color in morning mist\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">3. Seven-day pilot rhythm: from sandbox to controlled operation\u003C\u002Fh2>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Step 1: Build a task profile\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Align business, technology, and operations, and confirm one main scenario. Do not do customer service, marketing, and R&D assistants at the same time. The deliverables for the day are drafts of the task admission table and permission boundary table.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Step 2: Run normal historical samples\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Connect to masked historical data. First run common, complete, low-risk tasks to confirm that the Agent can output stable formats. In this step, do not chase impressive answers; chase reusable structure.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Step 3: Stress-test abnormal samples\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Prepare for missing fields, garbled text, and overly long text.\u003C\u002Fp>\n\u003Csection class=\"portal-disclaimer\" data-portal-disclaimer=\"1\" style=\"margin-top:2.5em;padding-top:1.25em;border-top:1px solid #e8e4dc;font-size:14px;line-height:1.7;color:#7a756c;\">\u003Cp style=\"margin:0;\">Disclaimer: This article is compiled from publicly available online sources for educational purposes only and does not represent the official position of Guanshan Academy. If you believe your rights have been infringed, please contact us for removal.\u003C\u002Fp>\u003Cp style=\"margin:0.75em 0 0;\">Contact: \u003Ca href=\"mailto:chenxj.g@gmail.com\">chenxj.g@gmail.com\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fsection>",3491395,"2026-09-29T12:05:43+08:00","2026-09-29T13:11:49.094504+08:00","2026-09-29T21:35:06.43814+08:00",{"id":147,"locale":13,"slug":148,"type":15,"title":149,"summary":150,"content_html":151,"video_url":19,"cover_url":54,"author":21,"category":22,"status":23,"source_job_id":152,"published_at":153,"created_at":154,"updated_at":155},146,"agent-不急着自动执行-规划-记忆-工具的四档上线清单","Don’t Rush Agents into Autonomous Execution: A Four-Tier Launch Checklist for Planning, Memory, and Tools","Based on public information, this article maps control points for agent planning, memory, and tools into a read-only, confirmation, restricted, and disabled acceptance checklist.","\u003Csection data-gzh-readable=\"1\" style=\"margin:0;padding:0;\">\u003Cdiv data-gzh-header=\"1\">\u003Cp style=\"font-size:15px;color:#888;text-align:center;margin-top:8px;\">Based on public information, this article maps control points for agent planning, memory, and tools into a read-only, confirmation, restricted, and disabled acceptance checklist.\u003C\u002Fp>\n\u003Cfigure class=\"portal-article-figure\">\u003Cimg src=\"https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Flandscape_mountain.jpg\" alt=\"观山静思\" loading=\"lazy\" decoding=\"async\" \u002F>\u003Cfigcaption>观山静思\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u003Cp style=\"font-size:14px;color:#999;text-align:center;margin-top:4px;\">Author: Jinxue Studio · Guanshan Academy\u003C\u002Fp>\u003Cp style=\"font-size:13px;color:#999;text-align:center;margin:0 0 6px 0;\">About 2,341 characters · Approx. 8-minute read\u003C\u002Fp>\u003Chr style=\"border:none;border-top:1px solid #eee;margin:24px 0;\" \u002F>\u003C\u002Fdiv>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">After reading this article, you can turn new developments in planning, memory, and tools into a four-tier acceptance checklist: what can be validated in read-only mode, what requires human confirmation, what can run under restrictions, and what should be disabled.\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">Background: The focus of Agents is shifting from answer quality to system control\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">According to public information, recent updates to Agent capabilities are focused not only on smoother answers, but on clearer task decomposition, retrieval, function calling, permission control, and rollback mechanisms.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Industry observations show that developers can now judge controllability with three questions: Can it be validated in read-only mode? Can it stop or roll back after failure? Can the evidence of actions be audited? These three questions are more reliable than testing model capability alone.\u003C\u002Fp>\u003Cp style=\"text-align:center;margin:16px 0 6px 0;\">\u003Cimg src=\"http:\u002F\u002Fmmbiz.qpic.cn\u002Fmmbiz_jpg\u002F4WyfSFibfkRND2scdegicSHFc74p94DWmL7VACuiaogHicIEmibHT8T5xFCt7SVicb8t7CJL4cRfxdffIU0lpxzaJGWwV5PiavVicoDEL5Qx7EibdJlA\u002F0?from=appmsg\" alt=\"Image: Garden path\" style=\"max-width:100%;border-radius:6px;display:block;margin:0 auto;\" \u002F>\u003C\u002Fp>\u003Cp style=\"text-align:center;font-size:12px;color:#bbb;margin:0 0 18px 0;\">Image: Garden path\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">Breaking Down New Developments: Control Points for Planning, Memory, and Tools\u003C\u002Fh2>\u003Cul>\u003Cli>According to public information: The new control point for planning is not the plan text itself, but validation before and after execution. A deployable plan should output steps, prerequisites, stop conditions, and evidence links, rather than only a goal description.\u003C\u002Fli>\u003Cli>According to public information: The new control points for memory are provenance, freshness, and permissions. Long-term memory entries should indicate where they came from, when they were written, and who is allowed to read them; otherwise, stale information and cross-user reads can become direct business risks.\u003C\u002Fli>\u003Cli>Industry observation: The new control points for tool calling are concentrated on schema validation and read-only-first execution. Queries, retrieval, and status reads can be validated early; database writes, configuration changes, outbound messages, and financial operations should first be simulated or approved.\u003C\u002Fli>\u003Cli>Industry observation: Tool integration approaches such as MCP increase the number of tools and also amplify integration risk. Tool discovery, invocation, responses, and logs need unified recording; otherwise, production issues cannot be reproduced.\u003C\u002Fli>\u003Cli>According to public information: Permission approval is shifting from one-time authorization to action-level grading. Different tools should have different thresholds: low-risk actions can be automated, while high-risk actions must be confirmed.\u003C\u002Fli>\u003Cli>Industry observation: Rollback capability has become a prerequisite for write operations. Changes that cannot demonstrate idempotence, compensation, or reversal should not enter the automated execution path.\u003C\u002Fli>\u003Cli>According to public information: Evidence chains are gradually becoming acceptance requirements. Final answers should preferably state which tool, which retrieval segment, or which failure reason they rely on, avoiding treating uncertain information as fact.\u003C\u002Fli>\u003Cli>Industry observation: Cost and budget controls are being brought into the Agent operating scope. Maximum steps, tool-call counts, timeout duration, and quota limits are basic guardrails before launching long-chain tasks.\u003C\u002Fli>\u003C\u002Ful>\u003Cp style=\"text-align:center;margin:16px 0 6px 0;\">\u003Cimg src=\"http:\u002F\u002Fmmbiz.qpic.cn\u002Fmmbiz_jpg\u002F4WyfSFibfkRNhtMwjNVndmatxwB026z8mxicOia2rHWt2YMjgpvBibKyfgia8UIkkGDRh3VrKWFWZg958p2pTpLC3koqp2ZgNBkPSRLL7mibnpsnY\u002F0?from=appmsg\" alt=\"Image: Soft light over spring fields\" style=\"max-width:100%;border-radius:6px;display:block;margin:0 auto;\" \u002F>\u003C\u002Fp>\u003Cp style=\"text-align:center;font-size:12px;color:#bbb;margin:0 0 18px 0;\">Image: Soft light over spring fields\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">A Comparison Table: Splitting Capabilities into Four Acceptance Tiers\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">You can classify current Agent capabilities into four tiers: read-only validation, human confirmation, restricted execution, and mandatory disablement. The control points in the table are suitable for shared use by development, testing, and launch reviews.\u003C\u002Fp>\u003Ctable style=\"width:100%;border-collapse:collapse;font-size:13px;margin:16px 0;\">\u003Ctr>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Acceptance tier\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Decision criteria\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Allowed actions\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Prohibited actions\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Use cases\u003C\u002Fth>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Read-only validation\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Can generate plans, query, and simulate tools, but cannot change external state\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Read documents, check configurations, retrieve memory, simulate function calls, generate evidence links\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Write to databases, change configurations, send external messages, perform real charges, trigger production changes\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Shadow evaluation before a new capability first enters production\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Human confirmation\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">The Agent can list executable steps and expected impacts, and executes only after user confirmation\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Submit change previews, wait for confirmation, execute single-step operations, record the confirmer\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Bulk publishing, cross-tenant writes, deleting data, modifying permissions, unconfirmed outbound messages\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">High-risk tools, bulk operations, and tasks that may change external state\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Restricted execution\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Automatic execution is allowed only when budget, step limits, permissions, logging, and rollback are satisfied\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Low-risk queries, cache writes, draft saving, and rollback-capable state updates\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Irreversible financial actions, privacy exports, production configuration changes, privilege escalation, and actions without logs\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Stable tools, mature scenarios, and actions whose results can be automatically verified\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Mandatory disablement\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Provenance cannot be demonstrated, auditing is impossible, stopping is impossible, or permissions are excessive\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Keep only an isolated demo environment; do not connect to real business systems\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Automatic transfers, automatic publishing, automatic deletion, automatic key changes, and cross-user reads\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Capabilities that demo well but have unclear control points should not go live, even if they run\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftable>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">Common Pitfalls: Why a Smooth Demo Still Leads to an Unstable Launch\u003C\u002Fh2>\u003Cul>\u003Cli>According to public information: Testing only the happy path overestimates Agent capability. Acceptance testing must include failure cases such as missing fields, tool timeouts, expired memory, permission denials, and empty results.\u003C\u002Fli>\u003Cli>Industry observation: Treating tool outputs as facts can amplify errors. Final answers should cite tool IDs, input summaries, result summaries, or failure reasons, rather than merely repeating tool text.\u003C\u002Fli>\u003Cli>According to public information: Memory and tools are prone to privilege overreach. Retrieval results also need permission checks, isolated by user, tenant, project, and sensitive fields.\u003C\u002Fli>\u003Cli>Industry observation: Long-chain planning can easily trigger multiple tool calls and cost fluctuations. Before launch, set maximum steps, timeouts, budgets, and manual interrupt switches.\u003C\u002Fli>\u003Cli>According to public information: Recording only the final answer is not enough. Intermediate steps, tool inputs, tool outputs, approval records, and failure reasons must all go into queryable logs.\u003C\u002Fli>\u003Cli>Industry observation: Frequent changes to tool schemas can destabilize Agents. Critical tools need version compatibility and field validation, returning explainable errors when fields are missing.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">Three Things Developers Can Do Today\u003C\u002Fh2>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">1. First list capabilities, then assign acceptance tiers\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Use one page to record the current Agent’s planning, memory, and tool capabilities. Label each item as read-only validation, human confirmation, restricted execution, or mandatory disablement. If it cannot be labeled, downgrade it to mandatory disablement first.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">2. Choose a low-risk scenario for read-only evaluation\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Do not write real data. Let the Agent only query information, call simulated tools, generate step drafts, and annotate sources. Observe whether it can identify gaps, refuse privilege overreach, and provide auditable evidence.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">3. Add three pieces of infrastructure before launch\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Operation logs, provenance annotations, and approval entry points. If any one is missing, do not open automated execution yet. Logs must be able to reconstruct every call, provenance must be traceable to a tool or retrieval item, and approvals must clearly record who confirmed which action.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Four-Tier Acceptance Checklist\u003C\u002Fh3>\u003Cul>\u003Cli>☐ Does planning have maximum steps, stop conditions, and failure-degradation paths?\u003C\u002Fli>\u003Cli>☐ Does planning output include prerequisites, evidence links, and expected impacts?\u003C\u002Fli>\u003Cli>☐ Do memory entries annotate source, write time, validity period, and visibility scope?\u003C\u002Fli>\u003Cli>☐ When retrieving memory, is isolation applied by user, tenant, project, and sensitive fields?\u003C\u002Fli>\u003Cli>☐ Do tool calls perform schema validation, and return explainable errors when fields are missing?\u003C\u002Fli>\u003Cli>☐ Do write operations distinguish three tiers: simulation, confirmation, and restricted execution?\u003C\u002Fli>\u003Cli>☐ Are outbound messages, financial actions, deletions, and permission changes set to human confirmation by default?\u003C\u002Fli>\u003Cli>☐ Can final answers cite tool IDs, retrieval IDs, result summaries, or failure reasons?\u003C\u002Fli>\u003Cli>☐ Do logs record discovery, invocation, responses, errors, approvals, and rollbacks?\u003C\u002Fli>\u003Cli>☐ Are failure cases prepared for missing fields, timeouts, permission denials, expired memory, and similar issues?\u003C\u002Fli>\u003C\u002Ful>\u003C\u002Fsection>\n\u003Csection class=\"portal-disclaimer\" data-portal-disclaimer=\"1\" style=\"margin-top:2.5em;padding-top:1.25em;border-top:1px solid #e8e4dc;font-size:14px;line-height:1.7;color:#7a756c;\">\u003Cp style=\"margin:0;\">Disclaimer: This article is compiled from publicly available online sources for educational purposes only and does not represent the official position of Guanshan Academy. If you believe your rights have been infringed, please contact us for removal.\u003C\u002Fp>\u003Cp style=\"margin:0.75em 0 0;\">Contact: \u003Ca href=\"mailto:chenxj.g@gmail.com\">chenxj.g@gmail.com\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fsection>",1311,"2026-09-29T10:58:21+08:00","2026-09-29T11:19:41.710784+08:00","2026-09-29T21:35:01.996456+08:00",{"id":157,"locale":13,"slug":158,"type":15,"title":159,"summary":160,"content_html":161,"video_url":19,"cover_url":54,"author":75,"category":22,"status":23,"source_job_id":162,"published_at":163,"created_at":164,"updated_at":165},125,"agent试点别只看输出-规划记忆工具三栏边界清单","Agent Pilots: Look Beyond Output—A Three-Column Boundary Checklist for Planning, Memory, and Tools","Use a three-column boundary table and a ☐ read-only pilot checklist to determine which agent planning, memory, and tool-call actions can be automated, which require approval, and which should be unavailable.","\u003Csection data-gzh-readable=\"1\" style=\"margin:0;padding:0;\">\u003Cdiv data-gzh-header=\"1\">\u003Cp style=\"font-size:15px;color:#888;text-align:center;margin-top:8px;\">Use a three-column boundary table and a ☐ read-only pilot checklist to determine which aspects of an agent’s planning, memory, and tool calls can be automated, which require approval, and which should be unavailable.\u003C\u002Fp>\n\u003Cfigure class=\"portal-article-figure\">\u003Cimg src=\"https:\u002F\u002Fguanshanshuyuan.cn\u002Fimages\u002Farticles\u002Flandscape_mountain.jpg\" alt=\"观山静思\" loading=\"lazy\" decoding=\"async\" \u002F>\u003Cfigcaption>观山静思\u003C\u002Ffigcaption>\u003C\u002Ffigure>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\u003Cp style=\"font-size:14px;color:#999;text-align:center;margin-top:4px;\">Author: Jinxuezhai · Guanshan Academy\u003C\u002Fp>\u003Cp style=\"font-size:13px;color:#999;text-align:center;margin:0 0 6px 0;\">Approx. 2,563 characters · About 9 minutes to read\u003C\u002Fp>\u003Chr style=\"border:none;border-top:1px solid #eee;margin:24px 0;\" \u002F>\u003C\u002Fdiv>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">Background: A Good Demo Does Not Mean Ready for Autonomy\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">According to public materials and industry observations, the most watched aspect of agents recently is not that their answers sound more human, but whether they can break a goal into controllable steps during long tasks and stop when the budget is exhausted.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">A single instruction in a demo can retrieve materials, write a draft, and call an API. In real work, risk may appear at step 7: retrieving irrelevant documents, carrying an old conclusion into a new task, or triggering an API that should not be written to.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">This article translates planning, memory, and tool calling into checkable boundaries: a three-column comparison table, a ☐ read-only pilot checklist, and three implementation actions.\u003C\u002Fp>\u003Cp style=\"text-align:center;margin:16px 0 6px 0;\">\u003Cimg src=\"http:\u002F\u002Fmmbiz.qpic.cn\u002Fsz_mmbiz_jpg\u002F4WyfSFibfkRPHib5l6X4lALib90TN9TZsAKvJlE4OSU2r20exJHic6sqepUKW4CpR4BX49jK6EYTcXBDkqZn55DiawYze6RlPO6uhmWswhUj0zyA\u002F0?from=appmsg\" alt=\"配图·庭园小径\" style=\"max-width:100%;border-radius:6px;display:block;margin:0 auto;\" \u002F>\u003C\u002Fp>\u003Cp style=\"text-align:center;font-size:12px;color:#bbb;margin:0 0 18px 0;\">Image: Garden path\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">Three Capability Lines: Planning, Memory, and Tools\u003C\u002Fh2>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Planning: From “Can Decompose” to “Can Stop”\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">According to industry practice, the new change in planning capability is moving from one-shot prompts to decomposition, reflection, and reordering. When engineered for real use, the question is not only whether steps are decomposed finely, but whether they are constrained by step limits, call counts, and failure branches.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">An agent without stopping conditions can drift farther and farther in a long task: repeatedly retrieving, repeatedly summarizing, and repeatedly revising prompts. You can start by giving each task four constraints: maximum number of steps, maximum number of tool calls, clear success criteria, and a failure threshold that triggers a stop.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Memory: From “Stuffing the Context” to Layered Isolation\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">According to public materials and industry observations, the new change in memory is not merely a longer context window, but the separation of working memory, long-term preferences, and process drafts. A window that holds more information does not mean the agent can reliably use the right information.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The most common memory problem is not “forgetting,” but “remembering what should not be used.” Temporary drafts treated as user preferences, previous-session content leaking into a new task, or sensitive information entering long-term memory can make an agent appear smart while actually being uncontrollable.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">You can start with three rules: process memory expires by default; long-term preferences require human confirmation; failed sessions are archived as read-only.\u003C\u002Fp>\u003Ch3 style=\"font-size:18px;font-weight:bold;color:#333;margin:1.6em 0 0.8em;\">Tool Calling: From Callable to Auditable\u003C\u002Fh3>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">According to public materials and industry observations, tool calling is moving from isolated function calls to standard interfaces, permission declarations, and call logs. Search, document reading, code execution, knowledge-base queries, and \u003Ccode style=\"font-family:'SF Mono',Menlo,Consolas,monospace;color:#333;background:#f5f5f5;padding:2px 6px;font-size:14px;border-radius:3px;\">MCP\u003C\u002Fcode>-style capabilities can all be connected, but what really determines whether they can go live is boundaries.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Tools should be divided into two classes: read-only and write. Reading materials and querying a knowledge base have a lower cost of failure; sending email, making payments, deleting records, changing configurations, and releasing to production have a much higher cost of failure.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">You can attach four labels to each tool: permission level, idempotency key, log entry point, and rollback method. Do not hand a tool without a rollback method to an autonomous agent.\u003C\u002Fp>\u003Cp style=\"text-align:center;margin:16px 0 6px 0;\">\u003Cimg src=\"http:\u002F\u002Fmmbiz.qpic.cn\u002Fmmbiz_jpg\u002F4WyfSFibfkROv8yBoI3S0vBfC5TSlMGQD6AYstG0pFSFXoCut2IFxWdCEw0LIFOicQ1LC9DmkITX3EOwWmoOLJdkDibpjYCVbP9Oicx7LYPFvUo\u002F0?from=appmsg\" alt=\"配图·春野柔光\" style=\"max-width:100%;border-radius:6px;display:block;margin:0 auto;\" \u002F>\u003C\u002Fp>\u003Cp style=\"text-align:center;font-size:12px;color:#bbb;margin:0 0 18px 0;\">Image: Soft light over a spring field\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">A Comparison Table: Turn Capability Boundaries into Checklist Items\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">This table is suitable to keep beside a pilot task. It does not discuss whether the model is strong or weak; it only answers: where the boundary is, what signals indicate crossing it, and what controls to apply after it is crossed.\u003C\u002Fp>\u003Ctable style=\"width:100%;border-collapse:collapse;font-size:13px;margin:16px 0;\">\u003Ctr>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Capability boundary\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Observable signals\u003C\u002Fth>\u003Cth style=\"border:1px solid #ddd;padding:8px 10px;background:#f5f8fc;color:#1a1a1a;font-weight:bold;text-align:left;\">Control actions\u003C\u002Fth>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Planning: whether it stays on the goal\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Steps keep increasing; output drifts from the original goal; repeated replanning; deliverables have no success criteria\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Limit the maximum number of steps and calls; pause when the budget is exceeded; require each step to state its input source or checkpoint\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Memory: whether it uses only the information it should use\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Old conclusions are mistaken for new facts; drafts enter long-term preferences; irrelevant content leaks across sessions; sensitive fields are not masked\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Set expiration for short-term memory; require human confirmation for long-term preferences; archive failed sessions as read-only; annotate retrieved results with source and update time\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Tools: whether they act within the authorized scope\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Calls an API outside the whitelist; a read-only task triggers a write; duplicate objects are created; there is no rollback path after failure\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Fix the tool whitelist; start with read-only permissions; route high-risk actions through an approval gate; record the operator and parameters for write operations\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Results: whether they can be reviewed\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Conclusions lack sources; citations are incomplete; results cannot be reproduced; errors cannot be traced to a step\u003C\u002Ftd>\u003Ctd style=\"border:1px solid #ddd;padding:8px 10px;vertical-align:top;\">Preserve the execution trace; save summaries at key nodes; attach a source list to the final result\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftable>\u003Cblockquote style=\"margin:1.2em 0;padding:0.8em 1em;border-left:3px solid #4a90d9;background:#f0f6fc;color:#444;font-size:15px;line-height:1.85;\">\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">Why this matters: these signals are not demo effects; they are the evidence for judging whether an agent can move from trial use to delegated autonomy.\u003C\u002Fp>\u003C\u002Fblockquote>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">☐ Read-Only Pilot Checklist\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The goal of a pilot is not to prove that the agent is powerful, but to expose its boundaries. First run it in an environment with no external-network writes, no real production database, and no real external sending.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">☐ Select two low-risk real tasks, such as material summarization, meeting-minutes drafts, or internal knowledge-base Q&A drafts.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">☐ Exclude write actions: external email sending, payments, file deletion, configuration changes, production releases, and customer-data modification.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">☐ Break each task into three columns: planned steps, required memory, and possible tool calls.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">☐ For each step, annotate the input source, permission scope, expected output, and stop condition.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">☐ First fix the tool whitelist, open only read permissions, and block all unapproved APIs.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">☐ Set budgets: maximum number of steps, maximum number of tool calls, and maximum number of replanning rounds.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">☐ During execution, record only three types of anomalies: plan drift, memory pollution, and tool overreach.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">☐ Preserve evidence: input for each step, output summaries, tool-call records, and failure reasons.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">☐ After the pilot, output three-tier conclusions: can be automated, requires approval, or cannot be used.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">☐ Configure a separate approval gate for actions that require approval; suspend integration for unusable actions and record the reason.\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">From Pilot to Autonomy: Progress by Risk\u003C\u002Fh2>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">The selection path can be simple: first fix the tool whitelist and read-only permissions, then run a read-only pilot; if tool overreach remains controllable, open autonomous planning; if memory pollution is still obvious, allow only retrieval memory and do not allow writes to long-term preferences.\u003C\u002Fp>\u003Cp style=\"font-size:16px;line-height:1.85;color:#333;margin:0 0 1em;text-align:justify;\">According to industry observation, scenarios suitable for early pilots include research organization, coding assistance, internal knowledge-base Q&A, and draft generation. Scenarios not suitable for direct autonomy include finance, production changes, external communications, customer-data modification, and contract approval. Their common risk is not that the task is important, but that errors are not easy to detect promptly.\u003C\u002Fp>\u003Ch2 style=\"font-size:20px;font-weight:bold;color:#1a1a1a;border-bottom:2px solid #4a90d9;padding-bottom:8px;margin:2em 0 1em;\">Three Common Pitfalls: Don’t Rush to Multi-Agent\u003C\u002Fh2>\u003C\u002Fsection>\n\u003Csection class=\"portal-disclaimer\" data-portal-disclaimer=\"1\" style=\"margin-top:2.5em;padding-top:1.25em;border-top:1px solid #e8e4dc;font-size:14px;line-height:1.7;color:#7a756c;\">\u003Cp style=\"margin:0;\">Disclaimer: This article is compiled from publicly available online sources for educational purposes only and does not represent the official position of Guanshan Academy. If you believe your rights have been infringed, please contact us for removal.\u003C\u002Fp>\u003Cp style=\"margin:0.75em 0 0;\">Contact: \u003Ca href=\"mailto:chenxj.g@gmail.com\">chenxj.g@gmail.com\u003C\u002Fa>\u003C\u002Fp>\u003C\u002Fsection>",1107,"2026-09-28T09:03:27+08:00","2026-09-29T02:03:45.863374+08:00","2026-09-29T21:35:02.099457+08:00",["Island",167],{"key":168,"result":169},"PortalBreadcrumb_MF3msh4VXBij2t7XBzDQhHZbfU2uXRMoXRHwZv2s",{"head":170},{"link":171,"style":172},[],[],["Island",174],{"key":175,"result":176},"PostCard_VOkjeTg8LIxAWmgdIcTKTu3ZI5z6tRcsvpCy9XWAtDM",{"head":177},{"link":178,"style":179},[],[],["Island",181],{"key":182,"result":183},"PostCard_zRDr9crDL9xzLTocHfUkInuXoyHTBCrIQn7CTKZN0",{"head":184},{"link":185,"style":186},[],[],["Island",188],{"key":189,"result":190},"PostCard_DFOAF3wKLSwzefs0XRmebnZByN0Db5wudM9fADuKNA",{"head":191},{"link":192,"style":193},[],[],["Island",195],{"key":196,"result":197},"PostCard_vlrURa70MGXdOYDKisZsOXeD0vd52zPXIbqLWCE",{"head":198},{"link":199,"style":200},[],[],["Island",202],{"key":203,"result":204},"PostCard_JyI08BM1QX2ZJyLkm0vGg6ieO8mjlPbeLdiK9Z6OgiE",{"head":205},{"link":206,"style":207},[],[],["Island",209],{"key":210,"result":211},"PostCard_JMxFuIS9B1GFIxsbIjY1JhxPr187KbPUOo1fkkZBKA",{"head":212},{"link":213,"style":214},[],[],["Island",216],{"key":217,"result":218},"PostCard_D3od0JSccUv8Kjd93fvfgAkB8b9Ds8PBPOUEYU5WAUI",{"head":219},{"link":220,"style":221},[],[],["Island",223],{"key":224,"result":225},"PostCard_JrUXUCvfWog9TPrNyf5IiRa4372qB5bX6EFiWTLVV4",{"head":226},{"link":227,"style":228},[],[],["Island",230],{"key":231,"result":232},"PostCard_bG6mrUneU5JCbROoSN1bgdh1BINBE2V2CmCvJfAfl94",{"head":233},{"link":234,"style":235},[],[],["Island",237],{"key":238,"result":239},"PostCard_cRpSUDgKKFxKOkRu0FYuANQ1KWlk6ZiZuAfsI0RJo",{"head":240},{"link":241,"style":242},[],[],["Island",244],{"key":245,"result":246},"PostCard_gvSRUbCr8BgX56CBdT6HXGuSWftk8SIDKWwtl7SQ4",{"head":247},{"link":248,"style":249},[],[],["Island",251],{"key":252,"result":253},"PostCard_qVnSyW5CafLrmq8OB4UIqGHRgjCytLkkCdOJUjgwl4",{"head":254},{"link":255,"style":256},[],[],["Island",258],{"key":259,"result":260},"PostCard_VNxug8BN4i0pDo6Xefe68sHp4H6bO1anGmB7UvkHHLk",{"head":261},{"link":262,"style":263},[],[],["Island",265],{"key":266,"result":267},"PostCard_WX58at8QjVb8brdSIeOq0ymnX46BHBIxDOlTHoHM",{"head":268},{"link":269,"style":270},[],[],["Island",272],{"key":273,"result":274},"PostCard_PFGJAPn2Z9E2vle2QYXkxMJJXzsJaTKv46KShm7HjqA",{"head":275},{"link":276,"style":277},[],[],1790693676388]