Agentic AI

Supervisor vs peer agents: which orchestration pattern should you pick?

Past two or three agents, one question always comes up: who decides who does what? Two architectures dominate.

The supervisor pattern

An orchestrator agent receives the task, breaks it down, delegates to specialized agents (research, writing, verification), then assembles the results.

Pros: predictable behavior, easier to debug (a single decision point), controlled cost since each sub-agent has a narrow role.

Limit: the supervisor becomes a bottleneck if the task requires many back-and-forths between sub-agents.

The peer agents pattern

Agents talk directly to each other (via a message queue or a shared board), with no single conductor.

Pros: more resilient if one agent goes down, better suited to tasks where the sequence of steps isn't known in advance.

Limit: debugging is harder — you have to trace agent-to-agent exchanges, not just a supervisor's calls.

Our recommendation

Always start with a supervisor. Only once you hit a real bottleneck (the supervisor grows too big, too much conditional logic) is it worth switching to peer agents — the extra debugging complexity isn't free.