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单代理与多代理：何时构建多代理系统
理解 AI 代理设计、ReAct 工作流程以及何时从单代理扩展到多代理系统的实用指南。单代理与多代理：何时构建多代理系统一文首先出现在 Towards Data Science 上。
来源:走向数据科学人工智能代理
When building an AI agent, the design choice matters.单个代理可能足以完成简单的任务，而更复杂的工作流程可能需要多个专门的代理一起工作，每个代理负责流程的特定部分，例如检索、编写、验证、编码、测试或审查。
这篇文章解释了 AI 代理设计的核心组件、ReAct 方法、单代理和多代理架构之间的区别，以及如何根据任务选择正确的设计。 It also includes a walkthrough of how a practical Multi-Agent RAG system works and how it was built.
popular because modern LLMs are now highly capable at tasks like coding, writing, reasoning, and solving problems across different fields. This has reduced the need to train custom models and shifted more attention toward building practical applications around existing LLMs. Codex、Claude Code、Cursor 和 Windsurf 等工具已经在帮助软件工程师更快地工作，而企业则使用代理来执行客户支持、自动化和其他实际任务。
AI 代理是一种使用 LLM 进行推理、规划和使用工具来执行任务的应用程序，允许模型以实用且有用的方式与其环境进行交互。
AI 代理的组件
Some of the major components of most AI agents are the LLM, tools, and memory.
ReAct (Reasoning + Acting) in Agents
An AI agent differs from a basic chatbot because a chatbot usually follows a more direct workflow:user query → LLM → response. The LLM receives the user’s message and generates a reply based mainly on the prompt and its existing context.
A basic ReAct workflow in an AI agent usually looks like this:
Step 1: The agent receives a user query
Step 2: The agent calls the required tools
Step 3: The tool outputs are sent back to the LLM