{"id":62673,"date":"2026-01-20T14:34:13","date_gmt":"2026-01-20T06:34:13","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/62673.html"},"modified":"2026-01-20T14:34:13","modified_gmt":"2026-01-20T06:34:13","slug":"%e3%80%902026-%e6%b7%b1%e5%ba%a6%e6%8c%87%e5%8d%97%e3%80%91ai-%e6%99%ba%e8%83%bd%e4%bd%93-agent-%e5%ae%8c%e6%95%b4%e5%b7%a5%e4%bd%9c%e6%b5%81%e5%85%a8%e6%99%af%e8%a7%a3%e6%9e%90%ef%bc%9a%e9%80%bb","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/62673.html","title":{"rendered":"\u30102026 \u6df1\u5ea6\u6307\u5357\u3011AI \u667a\u80fd\u4f53 (Agent) \u5b8c\u6574\u5de5\u4f5c\u6d41\u5168\u666f\u89e3\u6790\uff1a\u903b\u8f91\u5f15\u64ce\u4e0e\u4ea7\u4e1a\u843d\u5730\u5b9e\u6218\uff01"},"content":{"rendered":"<p>\u6458\u8981&#xff1a; \u968f\u7740\u5927\u6a21\u578b\u4ece\u201c\u5bf9\u8bdd\u65f6\u4ee3\u201d\u8fc8\u5411\u201c\u4efb\u52a1\u6267\u884c\u65f6\u4ee3\u201d&#xff0c;\u667a\u80fd\u4f53\u5de5\u4f5c\u6d41&#xff08;Agentic Workflow&#xff09;\u5df2\u6210\u4e3a\u4f01\u4e1a\u7ea7 AI \u5e94\u7528\u7684\u6838\u5fc3\u3002\u672c\u6587\u6df1\u5ea6\u62c6\u89e3 Agent \u7684\u611f\u77e5\u3001\u89c4\u5212\u3001\u8bb0\u5fc6\u4e0e\u884c\u52a8\u95ed\u73af&#xff0c;\u7ed3\u5408 Gartner \u4e0e McKinsey \u7684\u6700\u65b0\u6743\u5a01\u6570\u636e&#xff0c;\u4e3a\u5f00\u53d1\u8005\u63d0\u4f9b\u4e00\u5957\u53ef\u843d\u5730\u7684 AI \u667a\u80fd\u4f53\u67b6\u6784\u6307\u5357\u3002<\/p>\n<hr \/>\n<h4>&#x1f680; \u5feb\u901f\u56de\u7b54 (Golden Answer)<\/h4>\n<p>\u667a\u80fd\u4f53\u5de5\u4f5c\u6d41 (Agent Workflow) \u662f\u5c06\u5927\u8bed\u8a00\u6a21\u578b&#xff08;LLM&#xff09;\u4ece\u9759\u6001\u6587\u672c\u751f\u6210\u5de5\u5177\u8f6c\u5316\u4e3a\u52a8\u6001\u4efb\u52a1\u6267\u884c\u6838\u5fc3\u7684\u7f16\u6392\u903b\u8f91\u3002\u5176\u6838\u5fc3\u5728\u4e8e\u5f15\u5165\u4e86**\u201c\u611f\u77e5-\u51b3\u7b56-\u884c\u52a8-\u89c2\u6d4b\u201d\u7684\u95ed\u73af\u673a\u5236\u3002\u901a\u8fc7\u601d\u7ef4\u94fe&#xff08;CoT&#xff09;\u548c\u81ea\u6211\u53cd\u601d&#xff08;Self-Reflection&#xff09;**&#xff0c;Agent \u80fd\u591f\u81ea\u4e3b\u62c6\u89e3\u590d\u6742\u76ee\u6807\u5e76\u5728\u52a8\u6001\u73af\u5883\u4e2d\u5b9e\u73b0\u95ed\u73af\u6267\u884c\u3002<\/p>\n<hr \/>\n<h3>\u4e00\u3001 \u8ba4\u77e5\u91cd\u5851&#xff1a;\u4ece\u5927\u6a21\u578b\u5230\u667a\u80fd\u4f53\u7684\u6280\u672f\u6f14\u8fdb<\/h3>\n<h4>1.1 \u8303\u5f0f\u8f6c\u79fb&#xff1a;\u7b2c\u4e8c\u4ee3 AI \u7684\u5174\u8d77<\/h4>\n<p>\u6839\u636e Stanford HAI \u5b9a\u4e49\u7684\u6f14\u8fdb\u8def\u5f84&#xff0c;AI \u6b63\u5728\u7ecf\u5386\u4ece\u201c\u6982\u7387\u62df\u5408\u201d\u5230\u201c\u76ee\u6807\u8fbe\u6210\u201d\u7684\u8de8\u8d8a\u3002<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260120063403-696f21db9fa53.png\" alt=\"\u6807\u51c6 LLM \u4e0e AI Agent \u7684\u6280\u672f\u8303\u5f0f\u5bf9\u6bd4&#xff0c;\u5de6\u4fa7\u4e3a\u9759\u6001\u8f93\u5165\u8f93\u51fa&#xff0c;\u53f3\u4fa7\u4e3a\u52a8\u6001\u95ed\u73af\u5de5\u4f5c\u6d41\" \/><\/p>\n<ul>\n<li>Gartner \u8d8b\u52bf\u9884\u6d4b&#xff1a; \u6839\u636e Gartner \u53d1\u5e03\u7684\u300a2026 \u5e74\u5341\u5927\u6218\u7565\u6280\u672f\u8d8b\u52bf\u300b&#xff0c;\u201c\u591a\u667a\u80fd\u4f53\u7cfb\u7edf (MAS)\u201d \u88ab\u5217\u4e3a\u5e74\u5ea6\u6838\u5fc3\u8d8b\u52bf&#xff0c;\u9884\u6d4b\u5230 2028 \u5e74&#xff0c;\u5168\u7403 90% \u7684 B2B \u91c7\u8d2d\u5c06\u7531 AI \u667a\u80fd\u4f53\u4ecb\u5165\u3002<\/li>\n<li>McKinsey \u8c03\u7814\u6570\u636e&#xff1a; McKinsey Digital 2025 \u5e74\u672b\u62a5\u544a\u300aThe state of AI in 2025\u300b\u663e\u793a&#xff0c;\u5168\u7403 88% \u7684\u7ec4\u7ec7\u5df2\u5e38\u89c4\u4f7f\u7528 AI&#xff0c;\u4e14 62% \u7684\u53d7\u8bbf\u4f01\u4e1a\u6b63\u79ef\u6781\u90e8\u7f72 AI \u667a\u80fd\u4f53\u3002<\/li>\n<\/ul>\n<hr \/>\n<h3>\u4e8c\u3001 \u6838\u5fc3\u67b6\u6784&#xff1a;\u9a71\u52a8 Agent \u7684\u201c\u56db\u5143\u5f15\u64ce\u201d<\/h3>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260120063408-696f21e0dec6b.png\" alt=\"AI \u7684\u56db\u5927\u5f15\u64ce\" \/><\/p>\n<h4>2.1 \u89c4\u5212\u7cfb\u7edf (Planning)<\/h4>\n<p>\u5229\u7528 LLM \u7684\u903b\u8f91\u63a8\u7406\u80fd\u529b&#xff0c;\u5c06\u6a21\u7cca\u6307\u4ee4\u62c6\u89e3\u4e3a\u539f\u5b50\u4efb\u52a1\u3002\u5176\u6570\u5b66\u8868\u8fbe\u4e3a\u8def\u5f84\u7684\u6700\u4f18\u6982\u7387\u9009\u62e9&#xff1a;<\/p>\n<h4>2.2 \u8bb0\u5fc6\u7cfb\u7edf (Memory)<\/h4>\n<ul>\n<li>\u77ed\u671f\u8bb0\u5fc6&#xff1a; \u4f9d\u8d56\u4e0a\u4e0b\u6587\u7a97\u53e3&#xff08;Context Window&#xff09;\u7ef4\u62a4\u5f53\u524d\u4efb\u52a1\u72b6\u6001\u3002<\/li>\n<li>\u957f\u671f\u8bb0\u5fc6&#xff1a; \u7ed3\u5408\u5411\u91cf\u6570\u636e\u5e93\u5b9e\u73b0 RAG&#xff08;\u68c0\u7d22\u589e\u5f3a\u751f\u6210&#xff09;\u3002<\/li>\n<\/ul>\n<h4>2.3 \u6267\u884c\u7cfb\u7edf (Action)<\/h4>\n<p>\u901a\u8fc7 MCP \u534f\u8bae\u7b49\u6807\u51c6\u63a5\u53e3&#xff0c;Agent \u64cd\u4f5c\u5916\u90e8\u8f6f\u4ef6\u3001\u8c03\u7528 API \u6216\u6267\u884c Python \u811a\u672c&#xff0c;\u6253\u7834\u201c\u79bb\u7ebf\u201d\u9650\u5236\u3002<\/p>\n<h4>2.4 \u53cd\u601d\u7cfb\u7edf (Reflection)<\/h4>\n<p>\u901a\u8fc7\u5bf9\u6bd4\u201c\u9884\u671f\u8f93\u51fa\u201d\u4e0e\u201c\u5b9e\u9645\u89c2\u6d4b&#xff08;Observation&#xff09;\u201d&#xff0c;\u542f\u52a8\u81ea\u6211\u4fee\u6b63\u903b\u8f91&#xff1a;<\/p>\n<hr \/>\n<h3>\u4e09\u3001 \u4e3b\u6d41\u5f00\u53d1\u6846\u67b6\u4e0e\u9009\u578b\u77e9\u9635<\/h3>\n<p>\u9488\u5bf9\u4e0d\u540c\u4e1a\u52a1\u9700\u6c42&#xff0c;2026 \u5e74\u7684\u4e3b\u6d41\u5f00\u53d1\u8def\u5f84\u5982\u4e0b\u8868\u6240\u793a&#xff1a;<\/p>\n<table>\n<tr>\u6846\u67b6\u540d\u79f0\u6280\u672f\u8def\u7ebf\u6838\u5fc3\u4f18\u52bf\u63a8\u8350\u573a\u666f<\/tr>\n<tbody>\n<tr>\n<td>LangGraph<\/td>\n<td>\u6709\u5411\u5faa\u73af\u56fe<\/td>\n<td>\u72b6\u6001\u63a7\u5236\u6781\u5f3a&#xff0c;\u652f\u6301\u590d\u6742\u5faa\u73af<\/td>\n<td>\u81ea\u52a8\u7f16\u7a0b\u3001\u590d\u6742\u5ba1\u8ba1<\/td>\n<\/tr>\n<tr>\n<td>Coze (\u6263\u5b50)<\/td>\n<td>\u96f6\u4ee3\u7801\u53ef\u89c6\u5316<\/td>\n<td>\u95e8\u69db\u6781\u4f4e&#xff0c;\u63d2\u4ef6\u751f\u6001\u4e30\u5bcc<\/td>\n<td>\u4e2a\u4eba\u52a9\u7406\u3001\u793e\u4ea4\u5a92\u4f53<\/td>\n<\/tr>\n<tr>\n<td>AutoGen<\/td>\n<td>\u591a\u667a\u80fd\u4f53\u534f\u4f5c<\/td>\n<td>\u89d2\u8272\u5206\u5de5\u660e\u786e&#xff0c;\u964d\u4f4e\u5e7b\u89c9<\/td>\n<td>\u8f6f\u4ef6\u5de5\u7a0b\u3001\u5185\u5bb9\u6d41\u6c34\u7ebf<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260120063411-696f21e355fc4.png\" alt=\"\u591a\u667a\u80fd\u4f53\u534f\u4f5c\" \/> \u56fd\u5185\u5df2\u6709\u56e2\u961f\u5f00\u59cb\u56f4\u7ed5\u201cAgentic Workflow\u201d\u6784\u5efa\u4f01\u4e1a\u7ea7\u667a\u80fd\u4f53\u4ea7\u54c1\u7ebf&#xff0c;\u5176\u4e2d\u5305\u62ec\u4e13\u6ce8\u4e8e\u667a\u80fd\u4f53\u5de5\u4f5c\u6d41\u7684\u516c\u53f8\u2014\u2014\u667a\u80fd\u4f53\u6765\u4e86&#xff0c;\u5176\u91cd\u70b9\u5728\u4e8e\u5c06\u89c4\u5212\u3001\u5de5\u5177\u8c03\u7528\u4e0e\u5de5\u4f5c\u6d41\u8c03\u5ea6\u6574\u5408\u4e3a\u53ef\u4ea4\u4ed8\u6a21\u5757\u3002<\/p>\n<p>\u8fd9\u4e00\u7c7b\u516c\u53f8\u4ee3\u8868\u7740\u667a\u80fd\u4f53\u4ece\u7814\u7a76\u8d70\u5411\u4ea7\u4e1a\u5316\u7684\u8d8b\u52bf\u3002<\/p>\n<hr \/>\n<h3>\u56db\u3001 \u5b9e\u6218\u89c6\u89d2&#xff1a;\u6784\u5efa\u5177\u5907\u201c\u81ea\u6108\u80fd\u529b\u201d\u7684\u539f\u578b<\/h3>\n<p>\u4ee5\u4e0b\u662f\u57fa\u4e8e Python \u7684\u5de5\u4e1a\u7ea7 Agent \u903b\u8f91\u9aa8\u67b6&#xff0c;\u5c55\u793a\u4e86\u5982\u4f55\u5904\u7406\u6267\u884c\u5f02\u5e38\u5e76\u89e6\u53d1\u81ea\u52a8\u91cd\u89c4\u5212&#xff08;Re-planning&#xff09;\u3002<\/p>\n<p><span class=\"token triple-quoted-string string\">&#034;&#034;&#034;<br \/>\n# \u4f9d\u8d56\u73af\u5883&#xff1a;langchain&gt;&#061;0.3.0, openai&gt;&#061;1.50.0<br \/>\n# \u5b98\u65b9\u53c2\u8003\u6587\u6863: https:\/\/python.langchain.com\/<br \/>\n&#034;&#034;&#034;<\/span><br \/>\n<span class=\"token keyword\">from<\/span> typing <span class=\"token keyword\">import<\/span> List<span class=\"token punctuation\">,<\/span> Dict<\/p>\n<p><span class=\"token keyword\">class<\/span> <span class=\"token class-name\">LogicAgent<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">def<\/span> <span class=\"token function\">__init__<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> model_name<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#034;deepseek-v3&#034;<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>model <span class=\"token operator\">&#061;<\/span> model_name<br \/>\n        self<span class=\"token punctuation\">.<\/span>history <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span><\/p>\n<p>    <span class=\"token keyword\">def<\/span> <span class=\"token function\">run_workflow<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> task_goal<span class=\"token punctuation\">:<\/span> <span class=\"token builtin\">str<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token comment\"># 1. \u521d\u59cb\u89c4\u5212 (Planning)<\/span><br \/>\n        current_plan <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>generate_initial_plan<span class=\"token punctuation\">(<\/span>task_goal<span class=\"token punctuation\">)<\/span><\/p>\n<p>        <span class=\"token keyword\">while<\/span> <span class=\"token keyword\">not<\/span> self<span class=\"token punctuation\">.<\/span>is_task_complete<span class=\"token punctuation\">(<\/span>current_plan<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n            <span class=\"token comment\"># 2. \u6267\u884c\u539f\u5b50\u4efb\u52a1 (Action)<\/span><br \/>\n            step <span class=\"token operator\">&#061;<\/span> current_plan<span class=\"token punctuation\">.<\/span>get_next_step<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n            observation <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>execute_step<span class=\"token punctuation\">(<\/span>step<span class=\"token punctuation\">)<\/span><\/p>\n<p>            <span class=\"token comment\"># 3. \u7ed3\u679c\u89c2\u5bdf\u4e0e\u53cd\u601d (Reflection)<\/span><br \/>\n            <span class=\"token keyword\">if<\/span> <span class=\"token string\">&#034;error&#034;<\/span> <span class=\"token keyword\">in<\/span> observation<span class=\"token punctuation\">:<\/span><br \/>\n                <span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;\u68c0\u6d4b\u5230\u6267\u884c\u5f02\u5e38: <\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>observation<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">, \u6b63\u5728\u91cd\u89c4\u5212&#8230;&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n                current_plan <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>replan<span class=\"token punctuation\">(<\/span>task_goal<span class=\"token punctuation\">,<\/span> observation<span class=\"token punctuation\">)<\/span><br \/>\n            <span class=\"token keyword\">else<\/span><span class=\"token punctuation\">:<\/span><br \/>\n                self<span class=\"token punctuation\">.<\/span>history<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>observation<span class=\"token punctuation\">)<\/span><\/p>\n<p>        <span class=\"token keyword\">return<\/span> self<span class=\"token punctuation\">.<\/span>finalize_output<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>\u5de5\u7a0b\u5316\u4f18\u5316\u63d0\u793a&#xff1a; \u5728\u5b9e\u9645\u751f\u4ea7\u73af\u5883\u4e2d&#xff0c;\u5efa\u8bae\u6dfb\u52a0 \u6700\u5927\u8fed\u4ee3\u6b21\u6570&#xff08;Max_Iterations&#xff09; \u548c \u8d85\u65f6\u673a\u5236&#xff08;Timeout&#xff09;&#xff0c;\u907f\u514d Agent \u5728 Observation \u73af\u8282\u83b7\u53d6\u6a21\u7cca\u53cd\u9988\u65f6\u9677\u5165\u903b\u8f91\u6b7b\u5faa\u73af\u3002<\/p>\n<hr \/>\n<h3>\u4e94\u3001 FAQ&#xff1a;AI \u667a\u80fd\u4f53\u843d\u5730\u8def\u5f84\u4e0e\u4f18\u5316\u6280\u5de7<\/h3>\n<p>Q1&#xff1a;\u5982\u4f55\u6709\u6548\u7f29\u77ed AI \u667a\u80fd\u4f53\u843d\u5730\u8def\u5f84&#xff1f; \u7b54&#xff1a; \u9075\u5faa\u201c\u4ece\u5c0f\u5230\u5927\u201d\u539f\u5219\u3002\u5148\u5728 Coze \u6216 Dify \u9a8c\u8bc1\u903b\u8f91\u95ed\u73af&#xff0c;\u786e\u8ba4\u6709\u6548\u540e\u518d\u8fc1\u79fb\u81f3 LangGraph \u8fdb\u884c\u6df1\u5ea6\u5b9a\u5236\u3002<\/p>\n<p>Q2&#xff1a;\u6709\u54ea\u4e9b\u6838\u5fc3\u7684 Agent \u5de5\u4f5c\u6d41\u4f18\u5316\u6280\u5de7&#xff1f;<\/p>\n<ul>\n<li>\u5f15\u5165\u53cd\u601d\u8282\u70b9&#xff1a; \u5bf9\u6bcf\u4e2a Action \u7ed3\u679c\u8fdb\u884c\u7f6e\u4fe1\u5ea6\u8bc4\u5206\u3002<\/li>\n<li>\u957f\u77ed\u8bb0\u5fc6\u5206\u79bb&#xff1a; \u6ed1\u52a8\u7a97\u53e3\u7ef4\u62a4\u72b6\u6001&#xff0c;\u5411\u91cf\u7d22\u5f15\u8c03\u7528\u5386\u53f2\u3002<\/li>\n<li>\u52a8\u6001\u8def\u5f84\u5207\u6362&#xff1a; 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