[{"data":1,"prerenderedAt":765},["ShallowReactive",2],{"header-counts":3,"footer-counts":6,"wiki-ai-agent":9},{"tools":4,"reviews":5},65,7,{"tools":4,"reviews":5,"playbooks":7,"news":8},10,8,{"id":10,"title":11,"body":12,"category":742,"description":80,"extension":743,"meta":744,"navigation":745,"path":746,"published":747,"relatedModels":748,"relatedTools":751,"seo":757,"slug":758,"stem":759,"summary":760,"tags":761,"updated":747,"__hash__":764},"wiki\u002Fwiki\u002Fai-agent.md","AI Agent（智能体）",{"type":13,"value":14,"toc":716},"minimark",[15,20,24,59,66,70,81,86,89,95,99,102,108,112,115,119,123,137,155,159,179,183,203,207,210,321,324,328,332,389,392,436,440,510,513,516,548,552,598,605,608,640,643,687,690],[16,17,19],"h2",{"id":18},"什么是-ai-agent","什么是 AI Agent",[21,22,23],"p",{},"AI Agent（智能体）是一种 AI 系统，它能：",[25,26,27,35,41,47,53],"ol",{},[28,29,30,34],"li",{},[31,32,33],"strong",{},"感知"," — 理解用户意图和当前环境",[28,36,37,40],{},[31,38,39],{},"规划"," — 把复杂目标拆解为可执行的步骤",[28,42,43,46],{},[31,44,45],{},"行动"," — 调用工具、API、代码来完成每一步",[28,48,49,52],{},[31,50,51],{},"观察"," — 获取行动结果，判断是否成功",[28,54,55,58],{},[31,56,57],{},"迭代"," — 根据结果调整计划，持续直到完成",[21,60,61,62,65],{},"与普通聊天机器人的核心区别：",[31,63,64],{},"Agent 能自主决策和执行，不只是对话","。",[16,67,69],{"id":68},"agent-架构","Agent 架构",[71,72,77],"pre",{"className":73,"code":75,"language":76},[74],"language-text","用户目标\n  ↓\n┌─────────────────────────────────┐\n│         Agent 循环              │\n│                                 │\n│  ① 规划：下一步做什么？         │\n│  ② 行动：调用工具\u002F代码\u002FAPI      │\n│  ③ 观察：解析返回结果           │\n│  ④ 反思：成功了吗？需要调整？   │\n│  ⑤ 循环或结束                   │\n│                                 │\n└─────────────────────────────────┘\n  ↓\n任务完成\n","text",[78,79,75],"code",{"__ignoreMap":80},"",[82,83,85],"h3",{"id":84},"react-模式","ReAct 模式",[21,87,88],{},"最经典的 Agent 模式：Reasoning + Acting 交替进行。",[71,90,93],{"className":91,"code":92,"language":76},[74],"Thought: 用户要查上海的天气，我需要调用天气 API\nAction: call_weather_api(city=\"上海\")\nObservation: 上海今天 28°C，多云\nThought: 拿到天气数据了，可以回答用户了\nAnswer: 上海今天 28°C，多云，适合出行。\n",[78,94,92],{"__ignoreMap":80},[82,96,98],{"id":97},"plan-and-execute-模式","Plan-and-Execute 模式",[21,100,101],{},"先全局规划再逐步执行：",[71,103,106],{"className":104,"code":105,"language":76},[74],"Plan:\n  1. 搜索竞品信息\n  2. 提取价格数据\n  3. 生成对比表格\n  4. 写分析总结\n\nExecute step 1...\nExecute step 2...\n",[78,107,105],{"__ignoreMap":80},[82,109,111],{"id":110},"reflexion-self-correct-模式","Reflexion \u002F Self-Correct 模式",[21,113,114],{},"执行完一轮后让模型自己评审输出，发现问题再跑一次。代价是 token 翻倍，但能显著提升复杂任务的成功率。",[16,116,118],{"id":117},"agent-的关键能力","Agent 的关键能力",[82,120,122],{"id":121},"工具调用tool-use","工具调用（Tool Use）",[21,124,125,126,131,132,136],{},"Agent 通过 ",[127,128,130],"a",{"href":129},"\u002Fwiki\u002Ffunction-calling.html","function calling"," \u002F ",[127,133,135],{"href":134},"\u002Fwiki\u002Fmcp.html","MCP"," 调用外部工具：",[138,139,140,143,146,149,152],"ul",{},[28,141,142],{},"搜索引擎（Google \u002F Bing API）",[28,144,145],{},"代码执行（沙箱 Python \u002F Node.js）",[28,147,148],{},"文件操作（读写本地文件）",[28,150,151],{},"API 调用（HTTP 请求）",[28,153,154],{},"浏览器自动化（Puppeteer \u002F Playwright）",[82,156,158],{"id":157},"记忆memory","记忆（Memory）",[138,160,161,167,173],{},[28,162,163,166],{},[31,164,165],{},"短期记忆"," — 当前对话上下文（在 context window 内）",[28,168,169,172],{},[31,170,171],{},"长期记忆"," — 跨会话的知识存储（向量数据库）",[28,174,175,178],{},[31,176,177],{},"工作记忆"," — 当前任务的中间状态（变量、文件、中间结果）",[82,180,182],{"id":181},"规划planning","规划（Planning）",[138,184,185,191,197],{},[28,186,187,190],{},[31,188,189],{},"任务分解"," — 把\"做一个网站\"拆成\"设计→前端→后端→部署\"",[28,192,193,196],{},[31,194,195],{},"自我反思"," — 执行失败后分析原因，调整策略",[28,198,199,202],{},[31,200,201],{},"动态重规划"," — 发现原计划不可行时及时调整",[16,204,206],{"id":205},"agent-vs-copilot-vs-chatbot-vs-rpa","Agent vs Copilot vs Chatbot vs RPA",[21,208,209],{},"经常混淆的四个东西，一张表区分：",[211,212,213,235],"table",{},[214,215,216],"thead",{},[217,218,219,223,226,229,232],"tr",{},[220,221,222],"th",{},"维度",[220,224,225],{},"Chatbot",[220,227,228],{},"Copilot",[220,230,231],{},"Agent",[220,233,234],{},"RPA",[236,237,238,256,273,289,304],"tbody",{},[217,239,240,244,247,250,253],{},[241,242,243],"td",{},"主动性",[241,245,246],{},"被动答",[241,248,249],{},"辅助建议",[241,251,252],{},"自主执行",[241,254,255],{},"按固定流程跑",[217,257,258,261,264,267,270],{},[241,259,260],{},"决策",[241,262,263],{},"无",[241,265,266],{},"用户决策",[241,268,269],{},"Agent 决策",[241,271,272],{},"规则决策",[217,274,275,278,280,283,286],{},[241,276,277],{},"工具",[241,279,263],{},[241,281,282],{},"少量",[241,284,285],{},"任意",[241,287,288],{},"固定脚本",[217,290,291,294,296,298,301],{},[241,292,293],{},"适应变化",[241,295,263],{},[241,297,263],{},[241,299,300],{},"强",[241,302,303],{},"弱（页面变就崩）",[217,305,306,309,312,315,318],{},[241,307,308],{},"例子",[241,310,311],{},"早期客服 bot",[241,313,314],{},"GitHub Copilot",[241,316,317],{},"Devin \u002F Manus",[241,319,320],{},"UiPath \u002F 影刀",[21,322,323],{},"Agent 是 RPA 的下一代——RPA 写死流程，Agent 看懂目标自己想办法。",[16,325,327],{"id":326},"agent-分类","Agent 分类",[82,329,331],{"id":330},"按-autonomy-程度分","按 autonomy 程度分",[211,333,334,346],{},[214,335,336],{},[217,337,338,341,343],{},[220,339,340],{},"类型",[220,342,308],{},[220,344,345],{},"自主程度",[236,347,348,357,371],{},[217,349,350,352,354],{},[241,351,228],{},[241,353,314],{},[241,355,356],{},"低：辅助人类，不自主",[217,358,359,362,368],{},[241,360,361],{},"Tool-use Agent",[241,363,364],{},[127,365,367],{"href":366},"\u002Fcoding\u002Fcli\u002Fclaude-code.html","Claude Code",[241,369,370],{},"中：人类给目标，Agent 执行",[217,372,373,376,386],{},[241,374,375],{},"Autonomous Agent",[241,377,378,131,382],{},[127,379,381],{"href":380},"\u002Fcoding\u002Fagent\u002Fdevin.html","Devin",[127,383,385],{"href":384},"\u002Fagent\u002Fgeneral\u002Fmanus.html","Manus",[241,387,388],{},"高：给目标，Agent 全程自主",[82,390,391],{"id":391},"按场景分",[211,393,394,402],{},[214,395,396],{},[217,397,398,400],{},[220,399,340],{},[220,401,308],{},[236,403,404,412,420,428],{},[217,405,406,409],{},[241,407,408],{},"编程 Agent",[241,410,411],{},"Devin \u002F Cursor Composer \u002F Claude Code",[217,413,414,417],{},[241,415,416],{},"通用 Agent",[241,418,419],{},"Manus \u002F OpenManus",[217,421,422,425],{},[241,423,424],{},"桌面 Agent",[241,426,427],{},"OpenClaw \u002F AutoGLM",[217,429,430,433],{},[241,431,432],{},"Agent 平台",[241,434,435],{},"Coze \u002F Dify \u002F FastGPT",[16,437,439],{"id":438},"agent-平台对比","Agent 平台对比",[211,441,442,455],{},[214,443,444],{},[217,445,446,449,452],{},[220,447,448],{},"平台",[220,450,451],{},"定位",[220,453,454],{},"特点",[236,456,457,468,479,489,500],{},[217,458,459,462,465],{},[241,460,461],{},"Coze（扣子）",[241,463,464],{},"字节出品",[241,466,467],{},"低代码、模板丰富、国内直连",[217,469,470,473,476],{},[241,471,472],{},"Dify",[241,474,475],{},"开源",[241,477,478],{},"可私有化部署、RAG 内置",[217,480,481,484,486],{},[241,482,483],{},"FastGPT",[241,485,475],{},[241,487,488],{},"知识库优先、RAG 最强",[217,490,491,494,497],{},[241,492,493],{},"元器",[241,495,496],{},"百度出品",[241,498,499],{},"接入百度生态",[217,501,502,505,507],{},[241,503,504],{},"n8n",[241,506,475],{},[241,508,509],{},"工作流自动化、可视化编排",[16,511,512],{"id":512},"典型失败模式",[21,514,515],{},"知道 Agent 怎么失败，才知道怎么防。常见五种：",[25,517,518,524,530,536,542],{},[28,519,520,523],{},[31,521,522],{},"死循环"," — 模型一直觉得\"再试一次就成\"，反复调同一个失败的工具。防御：硬性步数上限 + 重复检测。",[28,525,526,529],{},[31,527,528],{},"跑偏目标"," — 多步任务中模型逐步把目标改成\"自己更想做的事\"。防御：每 N 步把原始目标重新塞回上下文。",[28,531,532,535],{},[31,533,534],{},"过度自信的错误结果"," — 工具返回了错误数据但模型不质疑，照单全收输出给用户。防御：critical 工具加 verifier + 让模型显式标注信心。",[28,537,538,541],{},[31,539,540],{},"副作用爆炸"," — 比如让 Agent 整理邮件，结果它\"自作主张\"开始删邮件。防御：默认 read-only，写操作单独 review。",[28,543,544,547],{},[31,545,546],{},"上下文窗口耗尽"," — 长任务下 working memory 越来越大，最后超限。防御：定期总结压缩历史、把工具输出截断。",[16,549,551],{"id":550},"自建-vs-用平台的决策","自建 vs 用平台的决策",[211,553,554,564],{},[214,555,556],{},[217,557,558,561],{},[220,559,560],{},"选择",[220,562,563],{},"适合场景",[236,565,566,574,582,590],{},[217,567,568,571],{},[241,569,570],{},"直接用 Coze \u002F Dify \u002F FastGPT",[241,572,573],{},"业务逻辑标准、内部使用、想快速看效果",[217,575,576,579],{},[241,577,578],{},"LangChain \u002F LlamaIndex 框架",[241,580,581],{},"需要自定义工作流但不想从零写",[217,583,584,587],{},[241,585,586],{},"自己写（基于 OpenAI SDK \u002F Anthropic SDK）",[241,588,589],{},"性能\u002F延迟敏感、有特殊业务约束、不想被框架绑死",[217,591,592,595],{},[241,593,594],{},"Claude Code \u002F Cursor 这类 Coding Agent",[241,596,597],{},"目标本身就是写代码",[21,599,600,601,604],{},"经验法则：",[31,602,603],{},"先用平台跑 PoC，跑通了再考虑自建","。从零写一个生产级 Agent 框架的工作量被严重低估。",[16,606,607],{"id":607},"当前局限",[25,609,610,616,622,628,634],{},[28,611,612,615],{},[31,613,614],{},"可靠性"," — 多步 Agent 的错误率随步骤数指数增长（10 步 × 90% = 35% 成功率）",[28,617,618,621],{},[31,619,620],{},"成本"," — 每步都要调用 LLM，复杂任务可能花费数美元",[28,623,624,627],{},[31,625,626],{},"速度"," — LLM 推理延迟 × 步骤数 = 等待时间长",[28,629,630,633],{},[31,631,632],{},"安全"," — Agent 能执行真实操作，出错后果严重（删文件、发邮件）",[28,635,636,639],{},[31,637,638],{},"上下文窗口"," — 长任务可能超出 context window",[16,641,642],{"id":642},"最佳实践",[25,644,645,651,657,663,669,675,681],{},[28,646,647,650],{},[31,648,649],{},"人在回路（Human-in-the-loop）"," — 关键步骤让人类确认",[28,652,653,656],{},[31,654,655],{},"沙箱执行"," — 代码在隔离环境运行，避免破坏系统",[28,658,659,662],{},[31,660,661],{},"限制工具范围"," — 只给 Agent 必要的工具权限",[28,664,665,668],{},[31,666,667],{},"检查点机制"," — 每步保存状态，失败可回滚",[28,670,671,674],{},[31,672,673],{},"超时与重试"," — 防止 Agent 陷入无限循环",[28,676,677,680],{},[31,678,679],{},"可观察性"," — 把每一步的 thought \u002F action \u002F observation 持久化，出问题能复盘",[28,682,683,686],{},[31,684,685],{},"预算硬上限"," — token \u002F 步数 \u002F 钱都要有硬上限，到顶停止而不是无限重试",[16,688,689],{"id":689},"延伸阅读",[138,691,692,703,709],{},[28,693,694,695,697,698,702],{},"协议层：",[127,696,135],{"href":134},"（Agent ↔ 工具）\u002F ",[127,699,701],{"href":700},"\u002Fwiki\u002Fa2a.html","A2A","（Agent ↔ Agent）",[28,704,705,706],{},"工具调用底层：",[127,707,708],{"href":129},"Function Calling",[28,710,711,712],{},"上下文管理：",[127,713,715],{"href":714},"\u002Fwiki\u002Fcontext-engineering.html","Context Engineering",{"title":80,"searchDepth":717,"depth":717,"links":718},3,[719,721,726,731,732,736,737,738,739,740,741],{"id":18,"depth":720,"text":19},2,{"id":68,"depth":720,"text":69,"children":722},[723,724,725],{"id":84,"depth":717,"text":85},{"id":97,"depth":717,"text":98},{"id":110,"depth":717,"text":111},{"id":117,"depth":720,"text":118,"children":727},[728,729,730],{"id":121,"depth":717,"text":122},{"id":157,"depth":717,"text":158},{"id":181,"depth":717,"text":182},{"id":205,"depth":720,"text":206},{"id":326,"depth":720,"text":327,"children":733},[734,735],{"id":330,"depth":717,"text":331},{"id":391,"depth":717,"text":391},{"id":438,"depth":720,"text":439},{"id":512,"depth":720,"text":512},{"id":550,"depth":720,"text":551},{"id":607,"depth":720,"text":607},{"id":642,"depth":720,"text":642},{"id":689,"depth":720,"text":689},"concept","md",{},true,"\u002Fwiki\u002Fai-agent","2026-06-21",[749,750],"claude-sonnet-4","gpt-5",[752,753,754,755,756],"agent\u002Fplatform\u002Fcoze","agent\u002Fplatform\u002Fdify","agent\u002Fgeneral\u002Fmanus","coding\u002Fagent\u002Fdevin","coding\u002Fcli\u002Fclaude-code",{"title":11,"description":80},"ai-agent","wiki\u002Fai-agent","能自主感知环境、规划任务、调用工具、持续迭代的 AI 系统，从聊天机器人进化到能干活的数字员工。",[231,762,763,39],"智能体","工具调用","XSXWfdT7aUU7OnpfUKa72KYyecYTn6DpyGWpt9mYWEc",1782316490722]