As tasks become more complex, a single AI agent often struggles to handle everything effectively. Planning, researching, reasoning, coding, and validating results can overwhelm one agent and lead to unreliable outputs.

A more scalable approach is to use multiple specialized agents working together.

A multi-agent system consists of several AI agents that collaborate to solve a problem. Each agent typically has a specific role or capability, and they communicate with one another to complete a larger task. Instead of one monolithic agent doing everything, the workload is divided across multiple agents that coordinate their efforts.

By distributing responsibilities, multi-agent systems can improve reliability, scalability, and reasoning quality.

In this chapter, we will explore how multi-agent systems work, common architectural patterns, and the coordination mechanisms that allow multiple agents to collaborate effectively.