In Depth
Multi-agent systems use multiple AI agents, each with specialized roles or capabilities, working together on complex tasks. Agents might have different expertise (research, coding, review), different perspectives (devil's advocate, optimist), or different responsibilities (planning, execution, quality assurance). They communicate through structured message passing and follow coordination protocols.
Common patterns include hierarchical systems (a manager agent delegates to worker agents), debate systems (agents argue different positions to refine solutions), pipeline systems (each agent handles a stage of processing), and collaborative systems (agents with complementary skills work in parallel). Frameworks like AutoGen, CrewAI, and MetaGPT implement various multi-agent patterns.
Multi-agent systems can solve problems that overwhelm single agents by distributing cognitive load and enabling specialization. A software development multi-agent system might include a product manager agent, architect agent, developer agents, and tester agent, each contributing their specialized capabilities. Challenges include coordination overhead, error propagation between agents, and the complexity of debugging multi-agent interactions. The field is rapidly evolving, with new coordination strategies and architectures emerging frequently.