How do you build multi-agent systems where agents collaborate?

Multi-agent systems coordinate multiple specialized AI agents that collaborate to solve complex problems. Each agent has defined expertise, tools, and responsibilities. Common architectures include hierarchical systems where a supervisor agent delegates subtasks to specialized worker agents, round-robin discussion where agents take turns contributing perspectives, debate patterns where agents argue different positions to reach better conclusions, and pipeline patterns where each agent processes and passes results to the next. Frameworks like AutoGen from Microsoft, CrewAI, and LangGraph enable building multi-agent systems. Key design considerations include clear role definition for each agent, communication protocols defining how agents share information, conflict resolution when agents disagree, shared memory for maintaining common context, and orchestration logic determining task routing and completion criteria. Multi-agent systems excel at tasks requiring diverse expertise like software development combining planning, coding, testing, and review agents.