Multiagent systems: a complete guide for production AI

Multiagent systems are the next evolution of AI applications. Instead of a single LLM call, you have multiple AI agents that plan, decide, and act — often coordinating with each other. This guide covers everything you need to know to build them for production.

What is a multiagent system?

A multiagent system is a collection of AI agents that work together to accomplish a task. Each agent has a specific role (e.g., researcher, writer, reviewer), and they communicate through a shared state or message bus. The key difference from a single-agent system is that agents can delegate, collaborate, and check each other's work.

When to use multiagent (and when not to)

• Use multiagent when: The task is complex, has multiple steps, and benefits from specialization
• Use multiagent when: You need human-in-the-loop approval at specific checkpoints
• Use multiagent when: Different steps require different tools or data sources
• Don't use multiagent when: A single LLM call with good prompting works fine
• Don't use multiagent when: Latency is critical (multiagent adds coordination overhead)

Architecture patterns

Three patterns we've seen work in production:

• Orchestrator-worker: One agent coordinates, others execute specific tasks
• Pipeline: Agents are chained sequentially, each passing output to the next
• Debate: Multiple agents propose solutions, a judge agent selects the best one

Governance and safety

Multiagent systems that take actions (not just generate text) need guardrails. Every action above a threshold should require human approval. Every action should be logged with the full reasoning trace. And every agent should have a clear boundary — what it can and cannot do.

If you're building a multiagent system and want to talk to engineers who've shipped them to production, book a discovery call. We'll share what's worked, what hasn't, and what we'd do differently.