Multi-Agent Systems: When and How to Use Them

A multi-agent system coordinates several AI agents, each with a role, to complete work one agent can't. Done well, it breaks big tasks into specialists. Done badly, it's N agents burning tokens talking to each other.

When multiple agents actually help

• The task needs distinct skills: research, write, review.
• Different context windows: one agent summarizes for another.
• Parallel work: independent subtasks run simultaneously.
• Separation of concerns: planning vs execution vs quality check.

When one agent is better

• The task is simple and linear.
• Quality drops from message-passing context loss.
• Token cost is a concern — coordination is expensive.
• Debugging complexity outweighs parallelism gains.

Orchestration patterns

1. Pipeline: agent A feeds agent B feeds agent C.
2. Orchestrator-worker: a lead delegates and assembles.
3. Debate/critic: agents challenge each other's output.
4. Hierarchical: managers plan, workers execute.

The coordination tax

Design rules

• Give each agent one clear responsibility.
• Define message formats between agents.
• Add a quality reviewer at the end.
• Set per-agent and total budgets.
• Log all handoffs for debugging.

FAQ