A multi-agent system is a setup where multiple autonomous agents work separately or together to complete a task, often with some form of coordination or agent orchestration.
A single agent is often enough for simple tasks, but multi-agent systems help when the problem is naturally split into parts: research, planning, execution, review, or parallel exploration. They can also let you assign different roles or tools to different agents, which is useful when one model instance would be too slow, too broad, or too error-prone.
In practice, teams reach for multi-agent systems when they want:
At a high level, each agent gets its own objective, context, and possibly its own tools or permissions. One agent may plan, another may search, and a third may verify results. The system then combines their outputs into a final answer or action.
Coordination is the core idea. That coordination can be simple, like a manager agent assigning tasks to worker agents, or more open-ended, like agents messaging each other until they converge on a solution. The exact protocol varies a lot across systems, so “multi-agent system” is a broad umbrella rather than one fixed architecture.
A common pattern is:
This is closely related to agent orchestration: the orchestration layer decides how agents are launched, how they share context, and when the system should stop.
Imagine a product team asking for a launch checklist:
A simple orchestration loop might look like:
Planner -> assigns research + drafting + review
Research agent -> collects facts
Writer agent -> creates checklist
Reviewer agent -> flags gaps
Planner -> returns final checklist
A good rule of thumb: start with a single agent and add a multi-agent design only when the task clearly benefits from decomposition, specialization, or parallelism.