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Core Concepts

Multi-Agent System

A multi-agent system is a setup where multiple AI agents, each with a narrower role, work on parts of a larger task and coordinate through some form of orchestration.

Researcher agentCoder agentReviewer agentOrchestrator
An orchestrator dispatches work to and collects results from specialized agents.

A single agent handling everything — research, writing code, reviewing it, testing it — tends to lose focus on long or varied tasks: the context needed for planning competes with the context needed for execution. A multi-agent system splits that work across separate agents, each given a narrower job and often a smaller, more specialized set of tools and instructions, then coordinates them so their outputs combine into one result.

A common shape is an orchestrator (or lead agent) that breaks a task into pieces and hands each piece to a specialized agent — a researcher, a coder, a reviewer — then assembles what comes back. The orchestrator itself doesn't do the specialized work; its job is delegation and coordination, sometimes running agents in parallel to save time, sometimes sequencing them because one agent's output feeds the next.

Multi-agent systems add real coordination overhead — agents can disagree, duplicate work, or need a shared source of truth — so they're generally worth the complexity when a task is large or heterogeneous enough that splitting it produces a clear win over one agent doing everything serially.

How it works

The diagram shows the typical shape: an orchestrator at the center dispatches work to specialized agents (here, researcher, coder, reviewer) and receives their results back — the double-headed arrows reflect that the orchestrator both assigns tasks and collects output, rather than the specialized agents talking directly to each other.

Example

A multi-agent system building a feature might use a researcher agent to look up how a library's API works, a coder agent to write the implementation using that research, and a reviewer agent to check the diff for bugs and style issues before it's proposed as a pull request — with an orchestrator sequencing the three.

How it differs

A multi-agent system is not the same as a subagent setup: a subagent is typically a lightweight, temporary delegate spawned by one main agent for a sub-task and then discarded, while a multi-agent system more broadly describes any architecture with multiple cooperating agents, which may or may not be organized as a main agent plus subagents.

Common misconceptions

Often assumed: More agents in the system means better results.
Actually: Splitting work across agents adds coordination overhead and failure modes like duplicated or conflicting work; it helps mainly when the task is large or varied enough to benefit from specialization.
Often assumed: Agents in a multi-agent system freely talk to each other like people in a group chat.
Actually: Most designs route communication through an orchestrator or a shared state rather than open peer-to-peer chat, to keep coordination predictable.

FAQ

What is a multi-agent system in AI?
It's an architecture where multiple specialized AI agents each handle part of a task and are coordinated, often by an orchestrator, so their work combines into one result.
What's the difference between a multi-agent system and subagents?
Subagents are usually lightweight delegates spawned by one main agent for a sub-task, while a multi-agent system is the broader category of any architecture with multiple cooperating agents.
When is a multi-agent system worth the added complexity?
Generally when a task is large or varied enough that splitting it across specialized agents outperforms one agent doing everything serially — small, narrow tasks usually don't need it.

Last checked: 2026-08-28

Related terms

Agent Team

An agent team is a multi-agent setup organized around role-based collaboration — a lead agent plus specialized members like a planner, coder, reviewer, and tester — modeled loosely on how a human team divides work.

Agent Orchestration

Agent orchestration is the layer that decides which agent, subagent, tool, or server handles a given piece of work, in what order, and how their results get combined.

Subagent

A subagent is a secondary AI agent that a main agent spawns to handle a specific sub-task, usually with its own focused instructions and tools, and reports back to the agent that spawned it.