How do multi-agent systems coordinate to solve complex tasks?

Multi-agent systems use specialized agents that collaborate through defined communication protocols. Architectures: hierarchical (manager agent delegates to specialist agents), flat/peer-to-peer (agents negotiate directly), and pipeline (sequential handoff between specialized stages). Coordination patterns: shared blackboard (agents read/write to a common state), message passing (agents communicate through structured messages), and auction-based (tasks are advertised and claimed by capable agents). Implementation with frameworks: CrewAI defines agents with roles, goals, and tools that execute tasks in a defined process; AutoGen creates conversational agents that collaborate through chat; LangGraph builds agent workflows as state machines with conditional routing. Key design decisions: agent specialization (each agent focuses on one domain for better performance), communication protocol (structured messages prevent misunderstanding), conflict resolution (how to handle contradictory recommendations), and termination conditions (when is the collective task complete). Real-world applications: software development (architect, coder, tester, reviewer agents), research (search agent, analysis agent, writing agent), and customer service (classifier agent, domain specialist agents, escalation agent). Challenges: debugging distributed reasoning, managing token costs across agents, and preventing infinite delegation loops.