What is a multi-agent system?

A multi-agent system splits work across several agents, each with a role. One might read the request, another might check the policy, another might draft the reply. They pass notes. A coordinator decides when the task is done.

More agents are not a better design. Each handoff can drop a fact or start a loop. Most companies should exhaust one well-scoped agent before they add a second. Split only when the roles are genuinely different and you can see each one's output.

Think of it this way: A multi-agent system is a project team rather than a solo employee. Each agent has a specialist role. They hand off work, check each other's output, and combine into a result no single agent could produce.

A content production system uses one agent to research, one to outline, one to write, and one to fact-check. The hand-offs are automatic. A human reviews the final draft, not every intermediate step.

A software team tried three agents on a bug report: one to reproduce, one to suggest a fix, one to write the test. The notes disagreed and nobody owned the conclusion. They went back to one agent that drafts a single write-up a developer reviews. The extra agents come back only if that write-up is the bottleneck.

When a task is genuinely too complex or too large for a single agent, or when parallel processing would significantly cut the time. Keep it simple until the simpler design breaks. Do not use multi-agent by default. Each added agent adds failure points, latency, and cost. A single well-designed agent handles most tasks more reliably than a premature team.

RaftLabs builds agents that do one defined job, with a person still on the steps that cannot be undone. You see the workflow on your own data before it runs on its own. The related work on our side is Multi-agent systems.

This sits with the other agents & automation terms on the glossary. When software stops only answering and starts carrying out the work. Worth reading next: AI Agent, Agentic AI, and Orchestration.