Multi Agent Collaboration
Some problems are easier when several focused agents cooperate instead of one agent doing everything. Each agent gets a narrow role, its own tools, and a clear interface to the others.
Common shapes
- An orchestrator plans and delegates subtasks to specialist workers.
- A pipeline passes output from one agent to the next in sequence.
- A debate has agents critique each other to surface errors before a final answer.
Why specialize
A focused agent has a shorter prompt, fewer tools, and a clearer objective, which usually means fewer mistakes. Splitting also enables parallelism: independent subtasks run at once instead of one long serial chain. Roles also make behavior easier to reason about than one sprawling generalist.
The coordination cost
Collaboration is not free. Agents must communicate, and every handoff is a chance to lose context or misunderstand intent. More agents mean more model calls, higher cost, and harder debugging when a failure hides inside one of many conversations. Multi agent designs pay off when the task genuinely decomposes; otherwise a single well prompted agent is simpler and cheaper.
Key idea
Multi agent systems split work across specialized cooperating agents, trading coordination overhead for clearer roles and parallelism.