Coordinating Multi-Agent Systems Without Chaos

It's tempting to throw more agents at a hard problem. But uncoordinated agents amplify each other's mistakes — they loop, contradict, and burn tokens debating. The value of a multi-agent system comes entirely from how it's coordinated.

Roles, not clones

Effective multi-agent systems assign distinct roles: a researcher that gathers, an executor that acts, a critic that verifies, a supervisor that routes. Each role has a narrow mandate and a focused toolset. Identical agents talking to each other rarely outperform a single well-designed one.

A supervisor gives you control

A supervisor pattern — one agent that decides which specialist handles each step — keeps the system legible. You can see why work was routed where, set policies on routing, and prevent the free-for-all that makes peer-to-peer agent swarms so hard to debug.

• Define each agent's mandate and tools narrowly
• Route through a supervisor for legibility
• Require a critic to verify before consequential actions
• Cap iterations to prevent infinite deliberation loops

Verification belongs to a separate agent

An agent grading its own work is unreliable. Separating the critic role — an agent whose only job is to check the executor's output against the goal — catches errors that the executor, anchored on its own plan, will miss every time.

In multi-agent systems, the critic is worth more than another executor.

Bound the loop

Every multi-agent loop needs hard limits: maximum iterations, token budgets, and timeouts. Without them, a disagreement between two agents becomes an expensive infinite loop. Coordination is as much about knowing when to stop as about who does what.