Multi-Agent System

A multi-agent system (MAS) is an architecture in which multiple autonomous AI agents collaborate, each with distinct roles and capabilities, to solve tasks too complex or large for a single agent to handle alone.

A multi-agent system is an architecture composed of two or more autonomous software agents that perceive their environment, make decisions, and take actions—individually or through coordination—to achieve shared or complementary goals. Each agent may have distinct capabilities, tools, or roles, enabling division of labor across complex workflows.

Agents in a MAS communicate through structured message passing, shared memory, or blackboard-style shared state. Some systems use a central orchestrator agent that routes subtasks to specialist agents; others rely on peer-to-peer coordination or market-style mechanisms in which agents bid for tasks. Modern implementations layer language models on top of tool-use and retrieval capabilities, so individual agents can reason, plan, and act within their assigned domain.

Multi-agent systems matter because they overcome the context-length, capability, and reliability ceilings of single-agent approaches. Distributing work across specialized agents allows parallelism, fault isolation, and the use of purpose-built models for each subtask. A coding agent, a retrieval agent, and a validation agent can operate concurrently on different parts of a problem, then merge results.

By 2026, frameworks such as Anthropic's Claude Agent SDK, Microsoft's AutoGen, LangChain's LangGraph, and Google's Agent Development Kit provide first-class primitives for building MAS. Production deployments span software engineering automation, enterprise workflow execution, and scientific research pipelines where dozens of agents collaborate within a single task.