{"id":114279,"date":"2026-10-08T18:48:59","date_gmt":"2026-10-08T10:48:59","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/114279.html"},"modified":"2026-10-08T18:48:59","modified_gmt":"2026-10-08T10:48:59","slug":"%e3%80%8aai-agent%e5%8e%9f%e7%90%86%e4%b8%8e%e5%ae%9e%e6%88%98%e3%80%8b2026%e7%89%88-%e7%ac%ac3%e8%af%be-agent-%e5%89%8d%e4%b8%96%e4%bb%8a%e7%94%9f%e4%b8%8e%e7%bb%8f%e5%85%b8%e8%8c%83%e5%bc%8f","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/114279.html","title":{"rendered":"\u300aAI Agent\u539f\u7406\u4e0e\u5b9e\u6218\u300b2026\u7248 \u7b2c3\u8bfe Agent \u524d\u4e16\u4eca\u751f\u4e0e\u7ecf\u5178\u8303\u5f0f"},"content":{"rendered":"<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261008104853-6ac7751584934.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<p>\u672c\u6587\u6536\u5f55\u4e8e\u4e13\u680f agent\u667a\u80fd\u4f53\u7cfb\u5217 \u2014\u2014 \u4e13\u680f\u7cfb\u7edf\u8986\u76d6 AI Agent \u7684\u8bb0\u5fc6\u3001\u5de5\u5177\u3001\u63d2\u4ef6\u4e0e\u5b9e\u6218&#xff0c;\u70b9\u51fb\u8ba2\u9605\u53ef\u8ddf\u8e2a\u540e\u7eed\u66f4\u65b0\u3002<\/p>\n<p>\u672c\u8bfe\u5b9a\u4f4d&#xff1a;Agent\u548cMLL\u7684\u8fd0\u884c\u673a\u5236\u61c2\u4e86&#xff0c;\u601d\u60f3\u4ece\u54ea\u6765&#xff1f;\u672c\u8bfe\u628a\u955c\u5934\u62c9\u8fdc&#xff1a;\u4ecb\u7ecd\u4ece\u7b26\u53f7\u4e3b\u4e49 Agent\u3001\u5f3a\u5316\u5b66\u4e60 Agent \u5230 LLM Agent \u7684\u4e09\u6761\u8c31\u7cfb&#xff0c;\u518d\u5230 ReAct\u3001Plan-and-Execute\u3001Reflexion\u3001ToT \u56db\u4e2a\u7ecf\u5178\u8303\u5f0f\u3002\u8303\u5f0f\u4e0d\u662f\u5386\u53f2\u6587\u7269\u2014\u2014\u7b2c 6 \u8bfe\u7684\u5faa\u73af\u91cc\u8dd1\u7684\u5c31\u662f ReAct&#xff0c;\u7b2c 22 \u8bfe\u7684\u7f16\u6392\u662f Plan-and-Execute \u7684\u4ea7\u54c1\u5316\u3002<\/p>\n<p>\u9636\u6bb5 1&#xff5c;\u6982\u5ff5\u4e0e\u57fa\u7840 &#xff5c; \u6765\u6e90&#xff1a;\u5438\u6536\u5fae\u8f6f\u8bfe\u7a0b\u5fae\u00b703 Agentic Design Principles \u4e0e\u5fae\u00b701 Agentic Patterns \u7684\u601d\u60f3 &#xff5c; \u77e5\u8bc6\u66f4\u65b0\u81f3 2026-10<\/p>\n<p>\u5173\u952e\u5b57&#xff1a; Agent\u53d1\u5c55\u53f2\u3001ReAct\u8303\u5f0f\u3001\u5f3a\u5316\u5b66\u4e60Agent\u3001\u7b26\u53f7\u4e3b\u4e49Agent\u3001Plan-and-Execute\u3001Reflexion\u3001\u601d\u7ef4\u6811ToT\u3001LLM Agent\u8d77\u6e90<\/p>\n<h3>\u672c\u8282\u76ee\u5f55<\/h3>\n<ul>\n<li>3.1 \u4e09\u6761\u8c31\u7cfb&#xff1a;Agent \u4e0d\u662f 2023 \u5e74\u624d\u6709\u7684<\/li>\n<li>3.2 ReAct&#xff1a;Thought \u2192 Action \u2192 Observation<\/li>\n<li>3.3 Plan-and-Execute \/ Reflexion \/ Tree of Thoughts&#xff1a;\u4e09\u6761\u6539\u8fdb\u8def\u7ebf<\/li>\n<li>3.4 \u4ece\u8303\u5f0f\u5230\u4ea7\u54c1&#xff1a;2023-2026 \u7684\u65f6\u95f4\u7ebf<\/li>\n<\/ul>\n<h3>\u6982\u5ff5\u8bb2\u89e3<\/h3>\n<h4>3.1 \u4e09\u6761\u8c31\u7cfb&#xff1a;Agent \u4e0d\u662f 2023 \u5e74\u624d\u6709\u7684<\/h4>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261008104854-6ac77516291ea.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<p>&#034;Agent&#xff08;\u667a\u80fd\u4f53&#xff09;&#034;\u662f AI \u7684\u539f\u751f\u6982\u5ff5&#xff0c;\u6bd4 LLM \u8001\u5f97\u591a\u3002\u7406\u89e3\u8c31\u7cfb\u80fd\u5e2e\u4f60\u770b\u6e05&#xff1a;\u4eca\u5929\u7684 LLM Agent \u590d\u5174&#xff0c;\u672c\u8d28\u662f\u65e7\u7684 Agent \u6846\u67b6\u6362\u4e0a\u4e86\u65b0\u7684\u51b3\u7b56\u6838\u5fc3\u3002<\/p>\n<p>\u7ecf\u5178\u6559\u79d1\u4e66\u5b9a\u4e49&#xff08;Russell &amp; Norvig&#xff09;&#xff1a;Agent &#061; \u611f\u77e5\u73af\u5883\u3001\u505a\u51fa\u51b3\u7b56\u3001\u91c7\u53d6\u884c\u52a8\u4ee5\u8fbe\u6210\u76ee\u6807\u7684\u5b9e\u4f53\u3002\u8fd9\u4e2a\u5b9a\u4e49\u8d2f\u7a7f\u4e09\u4ee3\u2014\u2014\u53d8\u7684\u662f&#034;\u51b3\u7b56&#034;\u600e\u4e48\u5b9e\u73b0&#xff1a;\u7b26\u53f7\u63a8\u7406 \u2192 \u5b66\u4e60\u5230\u7684\u7b56\u7565\u7f51\u7edc \u2192 \u5927\u6a21\u578b\u3002<\/p>\n<p>\u4e09\u4ee3\u5404\u81ea\u7684\u9057\u4ea7\u76f4\u63a5\u6d3b\u5728\u4eca\u5929\u7684 LLM Agent \u91cc&#xff1a;<\/p>\n<table>\n<tr>\u9057\u4ea7\u6765\u81ea\u5728 LLM Agent \u4e2d\u7684\u5316\u8eab<\/tr>\n<tbody>\n<tr>\n<td>\u4efb\u52a1\u5206\u89e3\u4e0e\u641c\u7d22<\/td>\n<td>\u7b26\u53f7\u4e3b\u4e49&#xff08;GPS \u7684\u624b\u6bb5-\u76ee\u7684\u5206\u6790&#xff09;<\/td>\n<td>Planner \/ Tree of Thoughts<\/td>\n<\/tr>\n<tr>\n<td>\u611f\u77e5-\u884c\u52a8\u5faa\u73af\u3001\u5956\u52b1\u4fe1\u53f7<\/td>\n<td>\u5f3a\u5316\u5b66\u4e60&#xff08;MDP \u6846\u67b6&#xff09;<\/td>\n<td>Agent loop\u3001\u73af\u5883\u53cd\u9988\u3001RL \u5fae\u8c03&#xff08;\u7b2c 31 \u8bfe&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u8bb0\u5fc6\u4e0e\u53cd\u601d<\/td>\n<td>\u8ba4\u77e5\u67b6\u6784&#xff08;SOAR\/ACT-R&#xff09;<\/td>\n<td>\u77ed\u671f\/\u957f\u671f\u8bb0\u5fc6\u3001Reflexion<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>LLM Agent \u7684\u8d77\u98de\u70b9\u662f 2022 \u5e74 10 \u6708\u7684 ReAct \u8bba\u6587&#xff08;arXiv:2210.03629&#xff0c;Yao et al.&#xff09;\u2014\u2014\u5b83\u7b2c\u4e00\u6b21\u628a&#034;\u63a8\u7406&#034;\u4e0e&#034;\u884c\u52a8&#034;\u4ea4\u7ec7\u6210\u7edf\u4e00\u5faa\u73af&#xff0c;\u5176\u540e\u51e0\u4e4e\u6240\u6709 Agent \u7cfb\u7edf\u90fd\u662f\u5b83\u7684\u53d8\u4f53\u6216\u7ec4\u5408\u3002<\/p>\n<h4>3.2 ReAct&#xff1a;Thought \u2192 Action \u2192 Observation<\/h4>\n<p>ReAct&#xff08;Reason &#043; Act&#xff09;\u7684\u5faa\u73af\u7ed3\u6784&#xff1a; <img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261008104855-6ac7751761c67.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<p>Question: \u54ea\u4e2a\u56fd\u5bb6\u7684\u4eba\u53e3\u662f\u65e5\u672c\u7684 1.5 \u500d\u4ee5\u4e0a\u4e14\u56fd\u571f\u76f8\u90bb&#xff1f;<br \/>\nThought 1: \u6211\u9700\u8981\u5148\u67e5\u65e5\u672c\u4eba\u53e3\u3002<br \/>\nAction 1: search[\u65e5\u672c \u4eba\u53e3]<br \/>\nObservation 1: \u65e5\u672c\u4eba\u53e3\u7ea6 1.24 \u4ebf&#xff08;2023 \u5e74&#xff09;\u3002<br \/>\nThought 2: 1.5 \u500d\u7ea6 1.86 \u4ebf\u3002\u5019\u9009\u90bb\u56fd\u91cc\u4fc4\u7f57\u65af\u8fdc\u8d85\u2026\u2026<br \/>\nAction 2: search[\u4fc4\u7f57\u65af \u4eba\u53e3]<br \/>\nObservation 2: \u4fc4\u7f57\u65af\u4eba\u53e3\u7ea6 1.44 \u4ebf\u2026\u2026<br \/>\nThought 3: \u4e0d\u591f 1.86 \u4ebf\u3002\u4e2d\u56fd\u4eba\u53e3\u7ea6 14 \u4ebf&#xff0c;\u6ee1\u8db3\u3002<br \/>\nAction 3: finish[\u4e2d\u56fd]<\/p>\n<p>\u4e09\u4e2a\u6210\u5206\u7684\u5206\u5de5&#xff1a;<\/p>\n<ul>\n<li>Thought&#xff08;\u601d\u8003&#xff09;&#xff1a;\u6a21\u578b\u663e\u5f0f\u5199\u51fa\u63a8\u7406\u2014\u2014\u51b3\u5b9a\u4e0b\u4e00\u6b65\u67e5\u4ec0\u4e48\u3001\u8bc4\u4f30\u5df2\u6709\u4fe1\u606f\u3002\u8fd9\u662f chain-of-thought \u7684\u884c\u52a8\u7248\u3002<\/li>\n<li>Action&#xff08;\u884c\u52a8&#xff09;&#xff1a;\u8c03\u7528\u5916\u90e8\u73af\u5883&#xff08;\u8bba\u6587\u91cc\u662f Wikipedia API&#xff09;\u3002<\/li>\n<li>Observation&#xff08;\u89c2\u5bdf&#xff09;&#xff1a;\u73af\u5883\u8fd4\u56de\u7ed3\u679c&#xff0c;\u8fdb\u5165\u4e0b\u4e00\u8f6e Thought\u3002<\/li>\n<\/ul>\n<p>ReAct \u8bba\u6587\u7684\u6838\u5fc3\u8d21\u732e\u662f\u8bc1\u660e\u53cc\u5411\u589e\u5f3a&#xff1a;\u63a8\u7406\u5f15\u5bfc\u884c\u52a8&#xff08;reason to act\u2014\u2014\u77e5\u9053\u8be5\u67e5\u4ec0\u4e48&#xff09;&#xff0c;\u884c\u52a8\u53cd\u54fa\u63a8\u7406&#xff08;act to reason\u2014\u2014\u67e5\u56de\u6765\u7684\u4e8b\u5b9e\u4fee\u6b63\u63a8\u7406&#xff09;\u3002\u5728 HotpotQA\/Fever \u4e0a&#xff0c;ReAct \u901a\u8fc7\u4e0e Wikipedia API \u4ea4\u4e92&#xff0c;\u7f13\u89e3\u4e86\u7eaf chain-of-thought \u7684\u5e7b\u89c9\u4e0e\u9519\u8bef\u4f20\u64ad&#xff1b;\u5728 ALFWorld\/WebShop \u4e24\u4e2a\u4ea4\u4e92\u73af\u5883\u4e0a&#xff0c;ReAct \u5206\u522b\u6bd4\u6a21\u4eff\u5b66\u4e60\u548c\u5f3a\u5316\u5b66\u4e60\u57fa\u7ebf\u9ad8\u51fa 34% \/ 10% \u7684\u7edd\u5bf9\u6210\u529f\u7387&#xff08;\u4ec5\u7528 1-2 \u4e2a\u793a\u4f8b\u505a few-shot&#xff09;\u3002<\/p>\n<p>\u4eca\u5929\u4f60\u53bb\u7528\u4efb\u4f55\u4e3b\u6d41 Agent&#xff08;Claude Code\u3001OpenAI \u6df1\u5ea6\u7814\u7a76\u3001\u5404\u5bb6\u7f16\u7801 Agent&#xff09;&#xff0c;\u5176\u9aa8\u67b6\u4ecd\u662f ReAct\u2014\u2014\u53ea\u662f Thought \u85cf\u8fdb\u4e86\u6a21\u578b\u7684 reasoning \u90e8\u5206&#xff0c;Action \u53d8\u6210\u4e86\u6807\u51c6 tool_calls \u534f\u8bae&#xff08;\u7b2c 2 \u8bfe&#xff09;\u3002<\/p>\n<h4>3.3 Plan-and-Execute \/ Reflexion \/ Tree of Thoughts&#xff1a;\u4e09\u6761\u6539\u8fdb\u8def\u7ebf<\/h4>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261008104856-6ac775183aaea.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<p>ReAct \u662f\u9010\u6b65\u53cd\u5e94\u5f0f\u7684&#xff1a;\u6bcf\u4e00\u6b65\u53ea\u770b\u5c40\u90e8\u3002\u5b83\u7684\u4e09\u4e2a\u5df2\u77e5\u5f31\u70b9\u53ca\u5bf9\u5e94\u6539\u8fdb\u8303\u5f0f&#xff1a;<\/p>\n<p>\u5f31\u70b9 1&#xff1a;\u957f\u4efb\u52a1\u6f02\u79fb\u2014\u2014\u8d70\u4e00\u6b65\u770b\u4e00\u6b65&#xff0c;\u76ee\u6807\u5bb9\u6613\u4e22\u3002\u2192 Plan-and-Execute&#xff08;\u5148\u89c4\u5212\u540e\u6267\u884c&#xff09;<\/p>\n<p>Plan: \u2460 \u67e5 X \u2461 \u67e5 Y \u2462 \u5bf9\u6bd4 \u2463 \u5199\u7ed3\u8bba<br \/>\nExecute: \u9010\u6b65\u6267\u884c\u8ba1\u5212&#xff0c;\u5fc5\u8981\u65f6 Replan<\/p>\n<p>\u89c4\u5212\u4e0e\u6267\u884c\u5206\u79bb\u7684\u601d\u60f3\u6765\u81ea Plan-and-Solve prompting&#xff08;Wang et al., ACL 2023, arXiv:2305.04091&#xff09;&#xff0c;LangChain \u5c06\u5176\u5de5\u7a0b\u5316\u4e3a Plan-and-Execute agent&#xff1a;Planner&#xff08;\u5927\u6a21\u578b&#xff0c;\u4e00\u6b21\u6027\u51fa\u5b8c\u6574\u8ba1\u5212&#xff09;&#043; Executor&#xff08;\u5c0f\u6a21\u578b&#xff0c;\u9010\u6b65\u6267\u884c&#xff09;&#043; Replanner&#xff08;\u5931\u8d25\u65f6\u91cd\u89c4\u5212&#xff09;\u3002\u4f18\u70b9&#xff1a;\u5927\u6a21\u578b\u8c03\u7528\u6b21\u6570\u5c11\u3001\u957f\u4efb\u52a1\u4e0d\u6f02\u79fb\u3001\u5927\u5c0f\u6a21\u578b\u5206\u5de5\u7701\u94b1&#xff1b;\u4ee3\u4ef7&#xff1a;\u8ba1\u5212\u53ef\u80fd\u8fc7\u65f6&#xff0c;\u9700\u8981 replan \u673a\u5236\u515c\u5e95\u3002<\/p>\n<p>\u5f31\u70b9 2&#xff1a;\u5931\u8d25\u4e0d\u590d\u76d8\u2014\u2014\u9519\u4e86\u5c31\u9519\u4e86&#xff0c;\u4e0b\u6b21\u8fd8\u9519\u3002\u2192 Reflexion&#xff08;\u8bed\u8a00\u5316\u53cd\u601d&#xff09;<\/p>\n<p>Trial 1: \u5c1d\u8bd5 \u2192 \u5931\u8d25&#xff08;\u6d4b\u8bd5\u4e0d\u8fc7&#xff09;<br \/>\nReflect : &#034;\u5931\u8d25\u56e0\u4e3a\u6ca1\u5904\u7406\u7a7a\u8f93\u5165&#xff0c;\u4e0b\u6b21\u8981\u5148\u505a\u8fb9\u754c\u68c0\u67e5&#034;<br \/>\nTrial 2: \u5e26\u7740\u53cd\u601d\u6587\u672c\u91cd\u8bd5 \u2192 \u901a\u8fc7<\/p>\n<p>Reflexion&#xff08;Shinn et al., NeurIPS 2023, arXiv:2303.11366&#xff09;\u63d0\u51fa&#034;verbal reinforcement learning&#034;&#xff1a;\u4e0d\u66f4\u65b0\u6743\u91cd&#xff0c;\u628a\u5931\u8d25\u6559\u8bad\u5199\u6210\u6587\u5b57\u5b58\u5165 episodic memory&#xff0c;\u4f5c\u4e3a\u4e0b\u4e00\u8f6e\u7684\u4e0a\u4e0b\u6587\u3002\u540d\u573a\u9762&#xff1a;HumanEval \u7f16\u7a0b\u57fa\u51c6\u4e0a Reflexion \u8fbe\u5230 91% pass&#064;1&#xff0c;\u8d85\u8fc7\u5f53\u65f6 GPT-4 \u76f4\u63a5\u751f\u6210\u7684 80%\u3002\u8fd9\u662f&#034;Agent \u81ea\u6211\u6539\u8fdb&#034;\u6700\u7b80\u5355\u53ef\u884c\u7684\u5f62\u6001&#xff08;\u7b2c 12 \u8bfe\u6df1\u5165\u5176\u5c40\u9650&#xff09;\u3002<\/p>\n<p>\u5f31\u70b9 3&#xff1a;\u5355\u94fe\u63a2\u7d22\u2014\u2014\u4e00\u6761\u8def\u8d70\u5230\u9ed1&#xff0c;\u4e0d\u80fd\u56de\u5934\u3002\u2192 Tree of Thoughts&#xff08;\u601d\u7ef4\u6811&#xff09;<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261008104857-6ac7751925a4c.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<p>ToT&#xff08;Yao et al., NeurIPS 2023, arXiv:2305.10601&#xff09;\u628a\u63a8\u7406\u7ec4\u7ec7\u6210\u6811&#xff1a;\u6bcf\u6b65\u751f\u6210\u591a\u4e2a\u5019\u9009&#034;thought&#034;&#xff0c;\u7528\u6a21\u578b\u81ea\u8bc4\u505a\u542f\u53d1\u5f0f\u526a\u679d&#xff0c;\u914d BFS\/DFS \u641c\u7d22&#xff0c;\u652f\u6301\u56de\u6eaf\u3002Game of 24 \u4efb\u52a1\u4e0a GPT-4 &#043; CoT \u6210\u529f\u7387 4%&#xff0c;ToT \u8fbe 74%\u3002\u4ee3\u4ef7\u662f\u8c03\u7528\u6b21\u6570\u66b4\u6da8&#xff08;\u6bcf\u4e2a\u8282\u70b9\u90fd\u662f\u4e00\u6b21\u63a8\u7406&#xff09;&#xff0c;\u751f\u4ea7 Agent \u4e2d\u591a\u7528\u4e8e\u79bb\u7ebf\u96be\u9898\u6c42\u89e3\u800c\u975e\u5728\u7ebf\u5faa\u73af\u3002<\/p>\n<p>\u4e00\u56fe\u603b\u89c8\u56db\u8303\u5f0f&#xff1a;<\/p>\n<table>\n<tr>\u8303\u5f0f\u51b3\u7b56\u65f6\u673a\u6838\u5fc3\u673a\u5236\u5f3a\u9879\u5f31\u9879<\/tr>\n<tbody>\n<tr>\n<td>ReAct<\/td>\n<td>\u6bcf\u6b65<\/td>\n<td>Thought-Action-Observation \u4ea4\u66ff<\/td>\n<td>\u901a\u7528\u3001\u5b9e\u65f6\u7ea0\u504f<\/td>\n<td>\u957f\u4efb\u52a1\u6f02\u79fb\u3001\u8d35<\/td>\n<\/tr>\n<tr>\n<td>Plan-and-Execute<\/td>\n<td>\u5f00\u5c40<\/td>\n<td>\u5148\u51fa\u5168\u8ba1\u5212\u518d\u6267\u884c<\/td>\n<td>\u7701\u5927\u6a21\u578b\u3001\u76ee\u6807\u7a33<\/td>\n<td>\u8ba1\u5212\u8fc7\u65f6\u9700 replan<\/td>\n<\/tr>\n<tr>\n<td>Reflexion<\/td>\n<td>\u5931\u8d25\u540e<\/td>\n<td>\u6587\u5b57\u53cd\u601d\u5165\u8bb0\u5fc6\u91cd\u8bd5<\/td>\n<td>\u81ea\u6211\u6539\u8fdb<\/td>\n<td>\u9700\u8981\u660e\u786e\u5931\u8d25\u4fe1\u53f7<\/td>\n<\/tr>\n<tr>\n<td>ToT<\/td>\n<td>\u6bcf\u6b65\u591a\u5206\u652f<\/td>\n<td>\u751f\u6210-\u8bc4\u4f30-\u641c\u7d22-\u56de\u6eaf<\/td>\n<td>\u96be\u9898\u63a2\u7d22<\/td>\n<td>\u8c03\u7528\u91cf\u7206\u70b8<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4>3.4 \u4ece\u8303\u5f0f\u5230\u4ea7\u54c1&#xff1a;2023-2026 \u7684\u65f6\u95f4\u7ebf<\/h4>\n<p><img decoding=\"async\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/10\/20261008104857-6ac77519e1518.png\" alt=\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\" \/><\/p>\n<ul>\n<li>2023 \u4e0a\u534a\u5e74&#xff1a;AutoGPT\/BabyAGI \u5f15\u7206&#034;\u81ea\u4e3b Agent&#034;\u60f3\u8c61\u2014\u2014\u4f46\u5b8c\u6210\u7387\u60e8\u6de1&#xff0c;\u66b4\u9732\u4e86\u89c4\u5212\u4e0e\u8bb0\u5fc6\u7684\u77ed\u677f\u3002<\/li>\n<li>2023-2024&#xff1a;\u8303\u5f0f\u6c89\u6dc0\u671f\u3002LangChain\/LlamaIndex \u628a ReAct\/Plan-Execute \u5de5\u5177\u5316&#xff1b;OpenAI function calling \u8ba9 Action \u6807\u51c6\u5316\u3002<\/li>\n<li>2024-2025&#xff1a;Anthropic\u300aBuilding Effective Agents\u300b&#xff08;2024-12-19&#xff09;\u7ed9\u884c\u4e1a\u6cfc\u51b7\u6c34\u53c8\u6307\u8def&#xff1a;\u591a\u6570\u573a\u666f workflow \u8db3\u591f&#xff0c;Agent \u7528\u5728\u5200\u5203\u4e0a&#xff1b;MCP \u534f\u8bae&#xff08;Anthropic 2024-11-25 \u5f00\u6e90\u53d1\u5e03&#xff09;\u7edf\u4e00\u5de5\u5177\u63a5\u5165\u3002<\/li>\n<li>2025-2026&#xff1a;\u7f16\u7801 Agent&#xff08;Claude Code\u3001Devin \u7c7b&#xff09;\u4e0e\u6df1\u5ea6\u7814\u7a76 Agent \u6210\u4e3a\u4e24\u7c7b\u6700\u5148\u89c4\u6a21\u843d\u5730\u7684\u5f62\u6001&#xff1b;\u591a Agent \u7f16\u6392\u3001Agentic RL \u6210\u4e3a\u524d\u6cbf&#xff08;\u7b2c 31 \u8bfe&#xff09;\u3002<\/li>\n<\/ul>\n<h3>\u6848\u4f8b\u5b9e\u6218&#xff1a;\u540c\u4e00\u4efb\u52a1&#xff0c;ReAct \u4e0e Plan-and-Execute \u7684\u884c\u4e3a\u5bf9\u6bd4<\/h3>\n<p>\u76ee\u6807&#xff1a;\u7528\u4e00\u4e2a\u771f\u5b9e\u4fe1\u606f\u68c0\u7d22\u4efb\u52a1&#xff0c;\u89c2\u5bdf\u4e24\u79cd\u8303\u5f0f\u7684\u884c\u4e3a\u5dee\u5f02&#xff0c;\u7406\u89e3\u5404\u81ea\u9002\u7528\u9762\u3002<\/p>\n<p>\u4efb\u52a1&#xff1a;\u201c\u67e5\u8bc1&#xff1a;\u73e0\u7a46\u6717\u739b\u5cf0\u7684\u9ad8\u5ea6\u4e58\u4ee5 3.28084 \u540e&#xff0c;\u662f\u5426\u5927\u4e8e 25000 \u82f1\u5c3a&#xff1f;\u201d<\/p>\n<p>\u6b65\u9aa4\u4e00&#xff1a;ReAct \u98ce\u683c\u8f68\u8ff9&#xff08;\u57fa\u4e8e\u7b2c 6 \u8bfe\u5c06\u5b9e\u73b0\u7684 mini agent&#xff0c;\u5de5\u5177&#061;search &#043; calculator&#xff0c;\u6b64\u5904\u5c55\u793a\u8f68\u8ff9\u5f62\u6001&#xff09;<\/p>\n<p>Thought 1: \u9700\u8981\u5148\u83b7\u53d6\u73e0\u5cf0\u51c6\u786e\u9ad8\u5ea6\u3002<br \/>\nAction 1: search(query&#061;&#034;\u73e0\u7a46\u6717\u739b\u5cf0 \u9ad8\u5ea6&#034;)<br \/>\nObservation 1: \u73e0\u7a46\u6717\u739b\u5cf0\u6d77\u62d4\u7ea6 8848.86 \u7c73&#xff08;2020 \u5e74\u6d4b\u5b9a&#xff09;\u3002<br \/>\nThought 2: \u73b0\u5728\u8ba1\u7b97 8848.86 \u00d7 3.28084\u3002<br \/>\nAction 2: calculator(expr&#061;&#034;8848.86*3.28084&#034;)<br \/>\nObservation 2: 29031.6938424<br \/>\nThought 3: 29031.69 &gt; 25000&#xff0c;\u7ed3\u8bba\u6210\u7acb\u3002\u4fe1\u606f\u5145\u5206&#xff0c;\u53ef\u4ee5\u56de\u7b54\u3002<br \/>\nAction 3: finish(\u662f&#xff0c;\u7ea6 29032 \u82f1\u5c3a &gt; 25000 \u82f1\u5c3a)<\/p>\n<p>\u6b65\u9aa4\u4e8c&#xff1a;Plan-and-Execute \u98ce\u683c\u8f68\u8ff9<\/p>\n<p>Plan: 1. \u68c0\u7d22\u73e0\u5cf0\u9ad8\u5ea6&#xff08;\u7c73&#xff09;<br \/>\n      2. \u8ba1\u7b97\u9ad8\u5ea6 \u00d7 3.28084&#xff08;\u82f1\u5c3a&#xff09;<br \/>\n      3. \u4e0e 25000 \u6bd4\u8f83\u5e76\u7ed9\u7ed3\u8bba<br \/>\nExecute step 1: search \u2192 8848.86 \u7c73<br \/>\nExecute step 2: calculator \u2192 29031.69<br \/>\nExecute step 3: 29031.69 &gt; 25000 \u2192 \u662f<br \/>\n(\u5168\u7a0b\u65e0 Replan)<\/p>\n<p>\u6b65\u9aa4\u4e09&#xff1a;\u5bf9\u6bd4\u5206\u6790\u2014\u2014\u4e24\u79cd\u8303\u5f0f\u5728\u672c\u4efb\u52a1\u4e0a\u7ed3\u679c\u4e00\u81f4&#xff0c;\u5dee\u5f02\u5728\u8fc7\u7a0b\u5c5e\u6027&#xff1a;<\/p>\n<table>\n<tr>\u89c2\u5bdfReActPlan-and-Execute<\/tr>\n<tbody>\n<tr>\n<td>\u5927\u6a21\u578b\u8c03\u7528<\/td>\n<td>3 \u6b21\u51b3\u7b56&#xff08;\u6bcf\u6b21\u90fd\u5e26\u5b8c\u6574\u601d\u8003&#xff09;<\/td>\n<td>1 \u6b21\u89c4\u5212 &#043; 3 \u6b21\u8f7b\u6267\u884c<\/td>\n<\/tr>\n<tr>\n<td>\u4e2d\u9014\u6539\u5411\u80fd\u529b<\/td>\n<td>\u5f3a&#xff08;Thought 2 \u53ef\u4e34\u65f6\u6362\u67e5\u6cd5&#xff09;<\/td>\n<td>\u5f31&#xff08;\u9664\u975e\u89e6\u53d1 replan&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u53ef\u5ba1\u8ba1\u6027<\/td>\n<td>\u8f68\u8ff9\u5373\u63a8\u7406\u8fc7\u7a0b<\/td>\n<td>\u8ba1\u5212\u5148\u884c&#xff0c;\u5408\u89c4\u53cb\u597d<\/td>\n<\/tr>\n<tr>\n<td>\u82e5 step 2 \u5de5\u5177\u62a5\u9519<\/td>\n<td>\u4e0b\u4e00\u8f6e Thought \u7acb\u5373\u5904\u7406<\/td>\n<td>\u9700\u8981\u663e\u5f0f replan \u5206\u652f<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u7ed3\u8bba&#xff1a;\u4e24\u6b65\u5c31\u80fd\u5b8c\u6210\u7684\u4efb\u52a1\u4e24\u8005\u65e0\u5dee\u522b&#xff1b;\u4efb\u52a1\u4e00\u957f&#xff08;10&#043; \u6b65&#xff09;\u3001\u6216\u9700\u8981\u5e76\u884c\u6267\u884c\u5b50\u4efb\u52a1&#xff0c;Plan-and-Execute \u7684\u6210\u672c\u4e0e\u7a33\u5b9a\u6027\u4f18\u52bf\u624d\u663e\u73b0&#xff1b;\u73af\u5883\u9ad8\u5ea6\u4e0d\u786e\u5b9a\u3001\u6bcf\u6b65\u90fd\u53ef\u80fd\u6539\u53d8\u7b56\u7565\u7684\u573a\u666f&#xff0c;ReAct \u7684\u53cd\u5e94\u6027\u66f4\u503c\u94b1\u3002\u8fd9\u4e5f\u662f\u7b2c 22 \u8bfe orchestrator-workers&#xff08;\u5148\u62c6\u5206\u518d\u5e76\u884c&#xff09;\u7684\u9009\u578b\u903b\u8f91\u3002<\/p>\n<p>\u5b8c\u6574\u4ee3\u7801&#xff1a;\u672c\u8bfe\u8f68\u8ff9\u4e3a\u793a\u610f&#xff08;\u57fa\u4e8e\u7b2c 6 \u8bfe\u5df2\u5b9e\u6d4b\u7684 mini_agent.py \u8fd0\u884c\u5f62\u6001\u6574\u7406&#xff0c;2026-10-01 \u5b9e\u6d4b\u8f93\u51fa\u8282\u9009&#xff1a;\u8c03\u7528 search({&#039;query&#039;: &#039;\u73e0\u7a46\u6717\u739b\u5cf0\u9ad8\u5ea6&#039;}) \u2192 \u8c03\u7528 calculator({&#039;expr&#039;: &#039;8848.86*3.28084&#039;}) \u2192 \u6700\u7ec8\u56de\u7b54 29031.69 \u82f1\u5c3a&#xff09;\u3002\u7b2c 6 \u8bfe\u63d0\u4f9b\u53ef\u8fd0\u884c\u5b8c\u6574\u5b9e\u73b0&#xff1b;Plan-and-Execute \u7684\u7f16\u6392\u601d\u60f3\u5728\u7b2c 9 \u8bfe\u7684 orchestrator-workers \u6a21\u5f0f\u4e2d\u5c55\u5f00\u3002<\/p>\n<h3>\u5c0f\u7ed3<\/h3>\n<ul>\n<li>Agent \u6982\u5ff5\u4e09\u4ee3\u540c\u5802&#xff1a;\u7b26\u53f7\u4e3b\u4e49\u7ed9\u4e86\u5206\u89e3\u4e0e\u641c\u7d22&#xff0c;\u5f3a\u5316\u5b66\u4e60\u7ed9\u4e86\u5faa\u73af\u4e0e\u5956\u52b1&#xff0c;LLM \u7ed9\u4e86\u901a\u7528\u8bed\u8a00\u51b3\u7b56\u6838\u5fc3\u3002<\/li>\n<li>ReAct&#xff08;2022-10&#xff09;\u662f LLM Agent \u7684\u8d77\u70b9\u8303\u5f0f&#xff1a;Thought-Action-Observation \u4ea4\u7ec7&#xff0c;\u63a8\u7406\u4e0e\u884c\u52a8\u4e92\u54fa\u3002<\/li>\n<li>\u4e09\u5927\u6539\u8fdb\u8def\u7ebf\u5bf9\u75c7 ReAct \u4e09\u5f31\u70b9&#xff1a;Plan-and-Execute \u6cbb\u6f02\u79fb\u3001Reflexion \u6cbb\u4e0d\u590d\u76d8\u3001ToT \u6cbb\u5355\u94fe\u4e0d\u56de\u5934\u3002<\/li>\n<li>\u8303\u5f0f\u9009\u62e9\u662f\u6210\u672c\/\u7a33\u5b9a\u6027\/\u53cd\u5e94\u6027\u7684\u4e09\u89d2\u6743\u8861&#xff0c;\u6ca1\u6709\u4e07\u80fd\u89e3&#xff1b;\u4ea7\u54c1\u5316\u4e3b\u7ebf\u662f&#034;workflow \u4f18\u5148&#xff0c;Agent \u70b9\u775b&#034;\u3002<\/li>\n<li>\u672c\u8bfe\u7684\u56db\u8303\u5f0f\u5c06\u5728\u7b2c 5 \u8bfe\u88ab\u88c5\u8fdb&#034;\u56db\u5927\u4ef6&#034;\u7ec4\u4ef6\u6846\u67b6\u91cd\u65b0\u6807\u6ce8&#xff0c;\u5728\u7b2c 9 \u8bfe\u6269\u5c55\u4e3a\u5b8c\u6574\u6a21\u5f0f\u4f53\u7cfb\u3002<\/li>\n<\/ul>\n<h3>\u6269\u5c55\u9605\u8bfb<\/h3>\n<p>&#x1f310; \u7f51\u7edc\u63d0\u793a&#xff1a;\u5b66\u672f\u7ad9 arxiv.org \u5883\u5185\u591a\u53ef\u76f4\u8fde&#xff08;\u6162&#xff09;&#xff0c;\u5176\u4f59\u8bc4\u6d4b\/\u89c4\u8303\u7ad9\u9700\u4ee3\u7406\u3002\u8be6\u89c1\u603b\u7eb2\u9644\u5f55\u300a\u56fd\u5185\u7f51\u7edc\u8bbf\u95ee\u4e0e\u56fd\u4ea7\u9002\u914d\u6307\u5357\u300b\u3002<\/p>\n<ul>\n<li>ReAct \u539f\u8bba\u6587&#xff1a;Yao et al., arXiv:2210.03629&#xff08;ICLR 2023&#xff09;https:\/\/arxiv.org\/abs\/2210.03629<\/li>\n<li>Reflexion&#xff1a;Shinn et al., arXiv:2303.11366&#xff08;NeurIPS 2023&#xff09;https:\/\/arxiv.org\/abs\/2303.11366<\/li>\n<li>Tree of Thoughts&#xff1a;Yao et al., arXiv:2305.10601&#xff08;NeurIPS 2023&#xff09;https:\/\/arxiv.org\/abs\/2305.10601<\/li>\n<li>Plan-and-Solve&#xff1a;Wang et al., arXiv:2305.04091&#xff08;ACL 2023&#xff09;https:\/\/arxiv.org\/abs\/2305.04091<\/li>\n<li>LLM Agent \u7efc\u8ff0&#xff08;329 \u7bc7\u6587\u732e&#xff09;&#xff1a;arXiv:2503.21460 https:\/\/arxiv.org\/abs\/2503.21460<\/li>\n<\/ul>\n<hr \/>\n<h3>\u6211\u7684\u4e13\u680f<\/h3>\n<table>\n<tr>\u86cb\u767d \/ \u6297\u4f53 \/ \u591a\u80bd \/ \u6838\u9178\u5206\u5b50\u6a21\u62df \/ \u52a8\u529b\u5b66 \/ \u5bf9\u63a5\u836f\u7269 \/ \u8bbe\u8ba1 \/ \u6848\u4f8bAI \/ Agent \/ \u5927\u6a21\u578b<\/tr>\n<tbody>\n<tr>\n<td>\u5f00\u6e90\u86cb\u767d\u7ed3\u6784\u9884\u6d4b<\/td>\n<td>\u5206\u5b50\u6a21\u62df\u57fa\u7840<\/td>\n<td>\u5c0f\u5206\u5b50\u836f\u7269\u8bbe\u8ba1\u6848\u4f8b<\/td>\n<td>AI Agent\u7cfb\u5217<\/td>\n<\/tr>\n<tr>\n<td>\u5f00\u6e90\u86cb\u767d\u751f\u6210\u65b9\u6cd5\u5b9e\u8df5<\/td>\n<td>\u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df-Amber<\/td>\n<td>\u86cb\u767d\u836f\u7269\u8bbe\u8ba1\u6848\u4f8b<\/td>\n<td>\u5316\u5b66\u5927\u6a21\u578b<\/td>\n<\/tr>\n<tr>\n<td>\u5f00\u6e90\u591a\u80bd\u8bbe\u8ba1\u6a21\u578b<\/td>\n<td>\u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df-Gromacs<\/td>\n<td>\u591a\u80bd\u836f\u7269\u8bbe\u8ba1\u6848\u4f8b<\/td>\n<td>\u300aAI Agent\u539f\u7406\u4e0e\u5b9e\u6218\u300b<\/td>\n<\/tr>\n<tr>\n<td>\u5f00\u6e90\u591a\u80bd\u6027\u8d28\u9884\u6d4b<\/td>\n<td>\u5206\u5b50\u52a8\u529b\u5b66\u6a21\u62df-OpenMM<\/td>\n<td>\u5f00\u6e90\u5c0f\u5206\u5b50\u751f\u6210\u8bbe\u8ba1<\/td>\n<td>CADD\u4e2d\u7684\u673a\u5668\u5b66\u4e60\u6a21\u578b<\/td>\n<\/tr>\n<tr>\n<td>DNA\/RNA\u836f\u7269\u8bbe\u8ba1<\/td>\n<td>\u7d50\u5408\u81ea\u7531\u80fd<\/td>\n<td>\u9ad8\u6548\u8ba1\u7b97\u914d\u7f6e<\/td>\n<td>\u300aMCP 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