agent-orchestrationlisted

# Agent Orchestration Best practices for designing, deploying, and coordinating AI agent systems using MCP servers, A2A protocol, and multi-agent patterns. ## Agent Orchestration Patterns Orchestration determines how agents are coordinated, who makes decisions, and how work flows between them. | Pattern | Description | Best For | Drawback | |---------|------------|----------|----------| | **Centralized** | Single orchestrator dispatches tasks to worker agents | Predictable workflows, clear task boundaries | Orchestrator is a bottleneck and single point of failure | | **Hierarchical** | Manager agents delegate to specialist sub-agents | Complex multi-domain tasks | Deep hierarchies add latency and lose context | | **Peer-to-peer** | Agents communicate directly, no central coordinator | Collaborative reasoning, brainstorming | Hard to debug, potential infinite loops | | **Pipeline** | Agents process sequentially, output feeds next agent | Data transformation, multi-stage analysis | Slow for parallelizable work, rigid ordering | | **Blackboard** | Shared state space that agents read from and write to | Problems requiring incremental refinement | Contention on shared state, ordering issues | | **Auction/Market** | Agents bid on tasks based on capability and capacity | Dynamic workload distribution | Overhead of bidding, suboptimal for simple tasks | | **Swarm** | Many lightweight agents with simple rules, emergent behavior | Exploration, search, large-scale parallel tasks | U