多智能体系统
定义
多智能体系统涉及多个 AI 智能体交互以解决任务: collaboration (divide work, share state), debate (argue and refine answers), or specialized roles (planner, executor, critic).
They extend single agents when one model or one loop is insufficient: 例如 one agent for RAG 检索, another for generation, another for critique. Subagents are a hierarchical form where a root agent delegates to children; here we focus on flat or peer-to-peer multi-agent patterns.
工作原理
用户向编排器（可以是 LLM 或固定工作流）发送任务。编排器将工作分配给 Agent1、Agent2 等tc., each with its own role, tools, and optionally model. Agents may share a common state, pass messages, or be invoked in sequence/parallel. Their outputs are aggregated (例如 combined, voted, or summarized) and returned to the user. Design choices include role assignment, communication protocol, and conflict resolution. MAS are useful when you want modularity (each agent has a clear responsibility), specialization (different models or tools per role), reusability (same agent in different workflows), and structured control flow.
应用场景
当单个智能体不够时，多智能体系统可以提供帮助：你需要不同的角色、辩论或模块化管道。
- Orchestrating planner, executor, and critic agents for complex tasks
- Debate or review flows where multiple agents refine an answer
- Specialized pipelines (例如 one agent for 检索, one for generation)
外部文档
- From Prototypes to Agents with ADK – Google Codelabs — ADK supports composing multiple agents into a multi-agent system
- LangChain – Multi-agent — Multi-agent orchestration patterns