Agent orchestration coordinates multiple specialized agents working on complex tasks. Architecture: Orchestrator agent routes tasks to specialists, manages state, handles inter-agent communication, aggregates results. Orchestration patterns: Sequential pipeline (A → B → C), parallel execution (fan-out/fan-in), hierarchical (manager-worker), dynamic routing based on task requirements. Components: Task queue, agent registry, communication protocol, state management, result aggregation. Framework features: LangGraph (stateful multi-agent), CrewAI (role-based teams), AutoGen (conversational agents), MetaGPT (software team simulation). Communication strategies: Shared memory/blackboard, message passing, hierarchical reporting. Coordination challenges: Deadlock prevention, failure handling, load balancing, context sharing. Example: Research orchestrator routes to search agent, analysis agent, writing agent, coordinates outputs. Best practices: Clear agent interfaces, minimal coupling, explicit handoffs, logging/observability, graceful degradation. Scalability: Horizontal agent scaling, async execution, caching common results. Essential for building production multi-agent systems.