A multi-agent system coordinates multiple AI agents — each with its own role, tools, and instructions — to accomplish tasks too complex for a single agent.
Instead of one generalist agent trying to do everything, you decompose the work:
- A research agent gathers information
- A writer agent synthesizes findings into a report
- A reviewer agent checks for accuracy and gaps
- An orchestrator routes work and manages handoffs
When to use multiple agents
Multi-agent architectures make sense when:
- Tasks have distinct phases that benefit from specialized prompts and tools
- Parallel work is possible (multiple research threads simultaneously)
- Quality checks need separation from generation (a reviewer shouldn't share the writer's context)
- A single agent's context window or capability set is insufficient
When one agent is enough
Many teams over-engineer multi-agent setups. A single well-architected agent with good tools often outperforms a swarm of poorly coordinated specialists. Start with one agent; split only when you hit clear bottlenecks.
Orchestration patterns
- Sequential pipeline — Agent A output feeds Agent B
- Supervisor — A lead agent delegates to specialists and synthesizes results
- Debate / consensus — Multiple agents propose solutions, a judge selects the best
Related reading
See Agent Orchestration for how multi-agent workflows are coordinated.