In short
A multi-agent system is a setup where several AI agents, each with its own role, tools and context, work together to complete a job that would be too large or too varied for a single agent. Instead of one agent trying to do everything in one long conversation, the work is split: an orchestrator agent breaks the goal into parts and hands each part to a specialised agent (research, coding, review), then combines their results. Each agent runs in its own context so it stays focused and does not drown in the whole task at once. The pattern trades some coordination overhead for specialisation, parallelism and cleaner context, which is why it shows up in the most ambitious agentic systems.
How multi-agent systems work
Most multi-agent systems follow an orchestrator-and-workers shape. A lead agent plans and delegates, worker agents do focused subtasks, and results flow back to be combined.
- Orchestrator: reads the goal, splits it into subtasks and decides who does what.
- Specialist agents: each owns a narrow role with its own prompt, tools and context window.
- Coordination: results are passed back and merged, sometimes across several rounds.
Multi-agent vs single agent vs subagents
A single agent handles a whole task in one context, which is simplest and best for most jobs. Subagents are the lightweight version of going multi-agent: a main agent delegates a focused piece to a helper that runs in its own context and returns a summary. A full multi-agent system goes further, with several peer agents coordinating over a shared goal. The honest rule is to reach for more agents only when one agent genuinely cannot hold the task, because every extra agent adds coordination cost.
When to use multiple agents
Multiple agents earn their keep when a job has distinct skills or can run in parallel: research many sources at once, or split building, testing and reviewing across specialists. They also help keep each context clean, since noisy subtasks stay out of the main thread. But more agents mean more ways to miscommunicate, so start with one capable agent and its subagents, and only grow into a full multi-agent system when the task clearly demands it.