Core Concepts

What Is Multi-Agent Systems?

Systems where multiple AI agents collaborate, each with specialized roles, to solve problems more effectively than a single agent.

In Plain English

A multi-agent system is like a small company of AI workers. Each agent has a defined role, specific tools, and expertise in one area. A research agent gathers data. A writing agent produces content. A review agent checks quality.

OpenClaw supports multi-agent systems through its agent routing and orchestration features. Agents can communicate, delegate tasks, and aggregate results.

The advantage over single-agent systems is specialization. An agent optimized for code review will outperform a general-purpose agent doing code review as one of many tasks.

Why It Matters for OpenClaw

As AI agents handle more complex work, single agents hit capability limits. Multi-agent systems scale by adding specialized agents rather than making one agent do everything. This is how AI teams will work in production.

How Clawctl Helps

Clawctl provides isolated multi-agent deployment with shared audit trails and approval workflows. Each agent in the system gets its own security controls while the orchestrator coordinates the team.

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Common Questions

When should I use multiple agents?

When a task requires different skills (research + writing), different tools, or different security levels.

Is it more expensive?

More agents = more LLM calls. But specialized agents are often cheaper because they use smaller/faster models for specific tasks.

Can agents disagree?

Yes. Debate patterns intentionally create disagreement to stress-test ideas. The orchestrator synthesizes the best outcome.