A multi-agent system is a setup where several AI agents, each with a specific role, work together on a task instead of one agent trying to do everything. One agent might research, another might write, and a third might review — coordinated by some form of orchestration.
The idea borrows from how teams work: rather than one generalist handling every part of a job badly, you split the work among specialists and coordinate their output.
Multi-agent systems add real complexity, so they're worth reaching for only when a single agent genuinely struggles with the scope or variety of a task — not by default.
Why use multiple agents instead of one
A single agent juggling research, writing, fact-checking, and formatting in one long prompt tends to blur all four jobs together — its context window fills with instructions for every role at once, and quality suffers on each.
Splitting the work gives each agent:
- A narrower, clearer job description (better instructions, better focus).
- Its own context window, uncluttered by unrelated steps.
- The ability to run in parallel with other agents, where the task allows it.
Common multi-agent roles
Most multi-agent systems use some version of these roles, even when the exact names differ:
| Role | Job | Example |
|---|---|---|
| Orchestrator / manager | Breaks the task into subtasks, assigns them, combines results | Decides research comes before writing |
| Specialist worker | Handles one narrow piece of the task | A "researcher" agent that only searches and summarizes |
| Reviewer / critic | Checks another agent's output before it's finalized | Flags a factual error in a draft |
| Tool-user | Focused specifically on calling one class of tools | An agent that only handles database queries |
Orchestration patterns
How agents hand off work to each other is called orchestration. A few common patterns:
- 1.Sequential — Agent A finishes, then hands its output to Agent B, and so on down a pipeline.
- 2.Manager-worker (hierarchical) — one orchestrator agent delegates subtasks to worker agents and assembles their results.
- 3.Conversational — agents talk to each other in a shared thread, similar to a group chat, until they converge on an answer.
- 4.Parallel-then-merge — independent agents work on separate pieces at the same time, and a final step merges their output.
A simple example
Imagine a "write a market research report" task split across three agents:
Orchestrator: "Research agent, find recent data on X.
Writer agent, draft a report once research is in.
Reviewer agent, check the draft for gaps before we ship it."
Research agent → gathers and summarizes sources
Writer agent → drafts the report using that summary
Reviewer agent → flags missing context, sends back for revisionEach agent only needs to be good at its one job — the orchestrator handles sequencing.
Multi-agent vs single-agent: when each makes sense
| Single agent | Multi-agent system | |
|---|---|---|
| Best for | Focused tasks with a clear, bounded scope | Tasks with genuinely distinct sub-skills or roles |
| Complexity | Lower — one loop to reason about | Higher — coordination, hand-offs, shared state |
| Cost | Lower — fewer model calls | Higher — each agent adds its own calls |
| Debugging | Easier to trace one loop | Harder — failures can hide in hand-offs between agents |
More agents isn't automatically better. A single well-scoped agent often outperforms a poorly coordinated multi-agent system doing the same job.
When multi-agent systems are worth the complexity
Reach for a multi-agent setup when a task has genuinely distinct sub-skills that don't share context well — research versus writing versus code review, for instance — or when parts of the work can run in parallel and independence speeds things up. Don't reach for it just because a single agent's prompt is getting long; often that's a sign the task needs better tools or memory, not more agents.
Frameworks that support multi-agent orchestration
A few frameworks are commonly used for this, each with a different philosophy: LangGraph treats orchestration as an explicit graph or state machine, which gives strong control over exactly how agents hand off work. CrewAI is built around defining agent "roles" quickly, which makes it fast to prototype a crew of specialists. AutoGen and Microsoft's Agent Framework lean into a conversational style, where agents talk to each other in a shared thread.
To go deeper on choosing between these, LangGraph vs CrewAI vs AutoGen compares them directly, and the agent loop is worth reading first if you haven't seen how a single agent's decide-act cycle works.
FAQ
What is a multi-agent system in AI?
A multi-agent system is a setup where multiple AI agents, each handling a distinct role or sub-task, work together and coordinate through some form of orchestration, rather than one agent handling the entire task alone.
How is orchestration different from just running multiple agents?
Orchestration is specifically about how agents hand off work and combine results — sequentially, through a manager agent, conversationally, or in parallel. Simply running several agents without a coordination pattern usually leads to duplicated work or conflicting outputs.
Do I need a multi-agent system for my project?
Probably not at first. Most tasks are better served by a single, well-scoped agent with good tools and clear instructions. Multi-agent systems earn their complexity when a task has genuinely distinct sub-skills or when parts of the work can run independently in parallel.
What's the easiest way to start learning multi-agent systems?
Start by understanding a single agent's loop thoroughly — how it decides, acts, and uses memory — before adding coordination between multiple agents. Trying to debug hand-offs between agents is much harder if you don't yet have a solid mental model of how one agent behaves on its own.