Multi-Agent Systems

A single AI agent handles one workflow well. But some tasks are too complex or too broad for one agent. Multi-agent systems split the work across specialized agents that each handle a piece of the problem.

Consider an example: processing a complex insurance claim. One agent reads and extracts data from the claim documents. Another agent checks the extracted data against policy rules. A third agent calculates the payout. A supervisor agent coordinates the whole flow and handles exceptions. Each agent is simpler and more reliable than one giant agent trying to do everything.

The coordination layer is what makes multi-agent systems different from just running multiple independent agents. Agents pass messages, share state, and follow a defined workflow. Some architectures use a central orchestrator that assigns tasks. Others use a more decentralized approach where agents negotiate and hand off work to each other.

Multi-agent systems work well for enterprise workflows that cross departments or require different types of expertise. Think of procurement processes, audit workflows, or customer onboarding journeys where multiple checks and approvals happen in sequence.

The tradeoff is complexity. Multi-agent systems are harder to build, test, and debug than single agents. You need clear contracts between agents and good observability to understand what is happening when something goes wrong.