Multi-Agent System

Definition

A multi-agent system, in the context of modern AI, is an architecture in which multiple distinct AI agents — often each built on an LLM with its own role, instructions, or toolset — collaborate, communicate, or negotiate with one another to accomplish a task that would be harder for a single agent to complete alone.

Overview

As LLM-based agents have matured beyond single-turn question answering into systems that can plan, use tools, and take multi-step actions, a natural extension has been to compose multiple such agents together rather than relying on one general-purpose agent to do everything. In a multi-agent system, individual agents are typically given specialized roles (for example, a 'planner' agent that breaks a task into steps, a 'researcher' agent that gathers information, a 'coder' agent that writes code, and a 'reviewer' agent that critiques output), and a orchestration layer or protocol governs how they communicate, hand off subtasks, and reach a final result. Common coordination patterns include sequential pipelines (each agent's output feeds the next), a manager/orchestrator pattern (a central agent delegates subtasks to specialist agents and integrates their responses), debate or critique patterns (multiple agents propose and critique each other's answers before converging, intended to surface errors a single agent might miss), and fully decentralized peer-to-peer communication. Open-source and commercial frameworks such as AutoGen, CrewAI, LangGraph, and OpenAI's Swarm/Agents SDK provide scaffolding for defining agent roles, message passing, and shared state or memory across agents, and Anthropic's Model Context Protocol has been used to standardize how agents discover and invoke shared tools within such systems. Multi-agent systems can improve performance on complex tasks by decomposing them into more tractable sub-problems each agent can specialize in, and by introducing checks (like a reviewer agent) that catch errors a single monolithic agent's output might contain. However, they also introduce new failure modes and costs: increased latency and token/compute cost from multiple LLM calls, coordination failures where agents miscommunicate or loop unproductively, compounding errors if one agent's mistake propagates unchecked to downstream agents, and added engineering complexity in designing robust handoff and error-recovery logic. Multi-agent design is an active area of both research and applied engineering, with ongoing debate over when the added complexity of multiple specialized agents outperforms a single well-designed agent with strong tool access.

Key Concepts

• Composes multiple specialized AI agents rather than relying on one general-purpose agent
• Common patterns: sequential pipelines, manager/orchestrator delegation, debate/critique, peer-to-peer
• Agents typically have distinct roles, instructions, and sometimes distinct toolsets
• Orchestration layer manages communication, task handoff, and shared state/memory
• Frameworks include AutoGen, CrewAI, LangGraph, and OpenAI's Agents SDK/Swarm
• Can improve task decomposition and error-catching via reviewer/critique agents
• Introduces added latency, cost, and coordination-failure risk compared to a single agent
• Model Context Protocol (MCP) is increasingly used to standardize shared tool access across agents

Use Cases

Frequently Asked Questions

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