What is a Multi-Agent System?
A multi-agent system contains multiple AI agents that interact, communicate, or coordinate within a shared environment or workflow to achieve individual or collective goals.
How does it work?
Agents in these systems can follow various patterns. Examples include:
- Specialized agents: Different agents handling specific domains (e.g., one writes code, another tests it).
- Supervisor-and-worker arrangements: A manager agent delegates tasks to sub-agents.
- Peer collaboration: Agents working side-by-side on a joint task.
- Debate or review: Agents arguing different perspectives to reach a better conclusion.
- Sequential handoffs: One agent completing a step and passing the result to the next.
What is a common misconception?
Adding several consecutive API calls to a language model does not automatically create a meaningful multi-agent system. True multi-agent systems require agents to have distinct identities, state, or decision-making capabilities that interact.
Why does it matter?
Multi-agent systems allow developers to break down overwhelmingly complex problems into manageable sub-tasks. By assigning specific personas and tools to different agents, the overall system becomes more robust and capable of solving multi-step, real-world problems.