Multi-agent systems explained
How multiple AI agents work together — when splitting work across specialised agents helps, the common coordination patterns, and why one good agent often beats five.
Many specialists instead of one generalist
A multi-agent system splits a problem across agents that each do one thing well, rather than asking a single agent to juggle everything in one long context. A common shape is a coordinator that reads the goal, breaks it into sub-tasks, and hands each to a specialist — one agent retrieves information, another drafts, another verifies — before merging the results into a final answer.
The appeal is the same as in a human team: division of labour, focus, and the ability to work parts in parallel. The cost is also the same: coordination, communication, and the chance that handoffs go wrong.
How agents are arranged
- Orchestrator-worker — a coordinator decomposes the goal and delegates sub-tasks to worker agents, then assembles the output.
- Pipeline — agents run in sequence, each transforming the previous one's output, like a relay.
- Reviewer or critic — one agent produces, a second checks the work against criteria before it is accepted.
- Peer collaboration — agents with different roles exchange messages to reach a result together.
Where the split pays off
Multiple agents earn their complexity when a task has parts that are genuinely parallel (research across many sources at once), genuinely specialised (a checker with different instructions from the writer), or that benefit from separation of concerns (keeping a privileged action behind a dedicated, narrowly-scoped agent). In these cases the split improves both quality and safety.
In the operators we run, a separate reviewer agent is one of the most reliable patterns: having a second agent check the first against explicit criteria catches mistakes that a single agent, marking its own homework, would miss.
Coordination is not free
Every added agent adds handoffs, and every handoff is a place to lose information, multiply cost, or compound an error. Debugging gets harder because failure can hide in the communication between agents rather than in any one of them. The discipline is to start with a single agent, prove it falls short, and only then introduce a second — keeping every agent's role, permissions, and outputs observable so the whole system stays auditable rather than a black box of agents talking to agents.
What is a multi-agent system?
A system in which several AI agents, each with a defined role, collaborate on a task. Usually a coordinator breaks the goal into sub-tasks and delegates them to specialist agents, then assembles their results into a final output.
When should I use multiple agents instead of one?
When a task has genuinely parallel parts, needs distinct specialists, or benefits from separating a privileged action behind its own scoped agent. If one well-built agent can do the job, prefer it — extra agents add coordination cost.
What are the common multi-agent patterns?
Orchestrator-worker (a coordinator delegates to workers), pipeline (agents run in sequence), reviewer or critic (one agent checks another), and peer collaboration (agents exchange messages). Orchestrator-worker and reviewer are the most controllable.
What are the downsides of multi-agent systems?
Added coordination cost, more places for information to be lost in handoffs, higher overall cost, and harder debugging — failures can hide in the communication between agents rather than in any single one.
Is a multi-agent system always better than a single agent?
No. More agents add complexity and new failure modes. Start with one agent, confirm it cannot do the job, and only then split the work — keeping every agent observable so the system stays auditable.
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