Single Agent vs Multi-Agent: When to Build a Multi-Agent System
Single Agent vs Multi-Agent: When to Build a Multi-Agent System
单智能体 vs 多智能体：何时构建多智能体系统
Agentic AI Single Agent vs Multi-Agent: When to Build a Multi-Agent System 智能体 AI：单智能体与多智能体——何时构建多智能体系统
A practical guide to understanding AI agent design, ReAct workflows, and when to scale from a single agent to a multi-agent system. Ayoola Olafenwa May 4, 2026 13 min read 这是一份关于理解 AI 智能体设计、ReAct 工作流以及何时从单智能体扩展到多智能体系统的实用指南。（作者：Ayoola Olafenwa，2026 年 5 月 4 日，阅读时长 13 分钟）
AI Agents AI 智能体
When building an AI agent, the design choice matters. A single agent may be enough for straightforward tasks, while more complex workflows may need multiple specialised agents working together, with each one responsible for a specific part of the process, such as retrieval, writing, verification, coding, testing or review. 在构建 AI 智能体时，设计选择至关重要。对于简单的任务，单个智能体可能就足够了；而更复杂的工作流则可能需要多个专业智能体协同工作，每个智能体负责流程中的特定部分，例如检索、写作、验证、编码、测试或审查。
This post explains the core components of AI agent design, the ReAct approach, the difference between single-agent and multi-agent architectures, and how to choose the right design depending on the task. It also includes a walkthrough of how a practical Multi-Agent RAG system works and how it was built. 本文解释了 AI 智能体设计的核心组件、ReAct 方法、单智能体与多智能体架构的区别，以及如何根据任务选择合适的设计。文中还包含了一个实用多智能体 RAG（检索增强生成）系统的工作原理及其构建过程的详细演示。
AI agents have become 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. AI 智能体之所以流行，是因为现代大语言模型（LLM）在编码、写作、推理和跨领域解决问题等方面表现出了极强的能力。这减少了对训练定制模型的需求，并将更多注意力转向了围绕现有 LLM 构建实际应用。
Tools like Codex, Claude Code, Cursor and Windsurf are already helping software engineers work faster, while businesses use agents for customer support, automation and other real-world tasks. An AI agent is an application that uses an LLM to reason, plan and use tools to perform tasks, allowing the model to interact with its environment in a practical and useful way. Codex、Claude Code、Cursor 和 Windsurf 等工具已经帮助软件工程师提高了工作效率，而企业则利用智能体进行客户支持、自动化处理及其他现实任务。AI 智能体是一种利用 LLM 进行推理、规划并使用工具来执行任务的应用程序，它使模型能够以实用且有效的方式与其环境进行交互。
Components of an AI Agent AI 智能体的组件
Some of the major components of most AI agents are the LLM, tools, and memory. 大多数 AI 智能体的主要组件包括 LLM、工具和记忆。
LLM: This is the brain of the AI agent. It is the large language model that enables the agent to reason, plan, and decide how to solve a given task. LLM（大语言模型）： 这是 AI 智能体的大脑。它是使智能体能够进行推理、规划并决定如何解决给定任务的大语言模型。
Tools: These are helpers, usually in the form of code functions, that allow the LLM to interact with its environment. Tools help the agent connect to external data sources, search the internet, retrieve information from databases, access files, and carry out specific actions. For example, coding agents can use tools to write, debug, and save files, research agents can use web search or vector databases to gather information and customer support agents can use internal company documents to answer questions based on trusted business knowledge. 工具： 这些是通常以代码函数形式存在的辅助程序，允许 LLM 与其环境进行交互。工具帮助智能体连接外部数据源、搜索互联网、从数据库检索信息、访问文件并执行特定操作。例如，编码智能体可以使用工具来编写、调试和保存文件；研究智能体可以使用网络搜索或向量数据库来收集信息；客户支持智能体可以使用公司内部文档，基于可信的业务知识来回答问题。
Memory: This allows the agent to store relevant information from interactions and use it later to provide better and more consistent assistance. It helps the agent maintain context across tasks and improve the overall user experience. Memory may be optional during early development, but it becomes an important part of many real-world AI agent systems, especially when the agent needs to handle follow-up questions, multi-step workflows or personalised interactions. There are two major types of memory commonly used in AI agents: short-term memory and long-term memory. Short-term memory keeps track of information within the current session or task, while long-term memory stores useful information across multiple sessions or chats so the agent can use it later. 记忆： 这允许智能体存储交互中的相关信息，并在后续使用这些信息以提供更好、更一致的帮助。它有助于智能体在不同任务间保持上下文，并改善整体用户体验。在开发初期，记忆可能是可选的，但它已成为许多现实 AI 智能体系统的重要组成部分，特别是当智能体需要处理后续问题、多步骤工作流或个性化交互时。AI 智能体中常用的记忆主要有两种类型：短期记忆和长期记忆。短期记忆用于跟踪当前会话或任务中的信息，而长期记忆则存储跨多个会话或聊天的有用信息，以便智能体在以后使用。
ReAct (Reasoning + Acting) in Agents 智能体中的 ReAct（推理 + 行动）
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. An AI agent goes beyond this by using the LLM to reason about the task, decide what needs to be done, choose whether tools are needed, call those tools, observe the results and continue until it can produce a useful answer. This is where the ReAct approach comes in. ReAct means Reasoning + Acting. It is an agent pattern where the LLM reasons about a task and takes actions, usually through tools, based on that reasoning. It involves designing a core logic loop around an LLM. AI 智能体与基础聊天机器人不同，因为聊天机器人通常遵循更直接的工作流：用户查询 → LLM → 回复。LLM 接收用户消息，并主要根据提示词及其现有上下文生成回复。AI 智能体则更进一步，它利用 LLM 对任务进行推理，决定需要做什么，选择是否需要工具，调用这些工具，观察结果并持续循环，直到能够产生有用的答案。这就是 ReAct 方法的用武之地。ReAct 意为“推理 + 行动”。这是一种智能体模式，LLM 基于对任务的推理采取行动（通常通过工具）。它涉及围绕 LLM 设计一个核心逻辑循环。
A basic ReAct workflow in an AI agent usually looks like this: AI 智能体中基本的 ReAct 工作流通常如下所示：
Step 1: The agent receives a user query The LLM reasons over the task and decides whether it can answer directly or needs to use tools. It checks what tools are available and decides which ones are needed to solve the task. 第一步：智能体接收用户查询 LLM 对任务进行推理，并决定是直接回答还是需要使用工具。它检查有哪些可用工具，并决定解决该任务需要哪些工具。
Step 2: The agent calls the required tools Based on its reasoning, the agent takes action by calling the necessary tools. These tools may search the web, retrieve documents from a vector database, access files, run code or connect to an external API. The results returned from these tools are known as tool outputs. 第二步：智能体调用所需工具 基于推理结果，智能体通过调用必要的工具采取行动。这些工具可以搜索网络、从向量数据库检索文档、访问文件、运行代码或连接外部 API。从这些工具返回的结果被称为“工具输出”。
Step 3: The tool outputs are sent back to the LLM The tool outputs are passed back to the LLM as additional context. This gives the agent more relevant information to work with instead of relying only on the original prompt. 第三步：工具输出被发送回 LLM 工具输出作为额外上下文传回给 LLM。这为智能体提供了更多相关信息，使其不必仅依赖原始提示词。
Step 4: The LLM checks the evidence and generates a response The LLM reviews the tool outputs and checks whether they are enough to solve the task. If the evidence is sufficient, it generates a grounded response for the user. If not, the agent may repeat the reasoning, tool-calling and observation steps until it has enough information to provide a useful answer. 第四步：LLM 检查证据并生成回复 LLM 审查工具输出，并检查它们是否足以解决任务。如果证据充足，它会为用户生成一个有据可查的回复。如果不足，智能体可能会重复推理、工具调用和观察步骤，直到获得足够的信息来提供有用的答案。
Structure of AI Agents AI 智能体的结构
AI Agents can either be single or multi depending on the design structure. 根据设计结构，AI 智能体可以是单智能体或多智能体。
Single Agent vs Multi-Agent 单智能体 vs 多智能体
A single agent is an agent design where one LLM handles the whole task. It reasons, plans and calls the required tools when needed. Most AI agents start as single-agent systems because they are simpler, easier to maintain and usually enough for many tasks. 单智能体是一种由一个 LLM 处理整个任务的智能体设计。它负责推理、规划并在需要时调用所需工具。大多数 AI 智能体最初都是作为单智能体系统构建的，因为它们更简单、更易于维护，且通常足以胜任许多任务。
A multi-agent system uses specialised agents to solve different parts of a task. It often has a central agent, usually called an orchestrator, supervisor or planner, that coordinates the other agents and decides when each one should act. Each specialised agent can have its own role, tools and reasoning logic, making the system more modular and suitable for complex workflows. 多智能体系统使用专业智能体来解决任务的不同部分。它通常有一个中央智能体（通常称为编排器、监督者或规划者），负责协调其他智能体并决定每个智能体何时行动。每个专业智能体都可以拥有自己的角色、工具和推理逻辑，这使得系统更具模块化，更适合复杂的工作流。
When to Build A Multi-Agent System 何时构建多智能体系统
A single-agent design… 单智能体设计……