Don't Panic - AI Agent Orchestration

AI agent orchestration coordinates specialized agents so they can finish work no single agent handles well. Each agent has a role, tools, and boundaries. The orchestration layer routes subtasks, passes context, manages shared state, and recovers when a step fails.

Start with patterns, not frameworks. Supervisor-worker routes from a central planner to specialists and aggregates results. Hierarchical systems add planners and coordinators above workers. Pipelines run agents in a fixed sequence. Peer-to-peer agents negotiate without a center. Pick the pattern from task shape: predictable decomposition favors a supervisor; deterministic stages favor a pipeline; decentralized control needs peers.

Delegation has four mechanical parts. Decomposition breaks the objective into assignable subtasks. Routing matches each subtask to a capable agent. Context propagation sends enough history without overflowing the window. Aggregation combines partial results and resolves conflicts. Missing any part produces incomplete work or incoherent answers.

Communication is becoming standardized. MCP connects agents to tools. A2A covers agent discovery and messaging across frameworks. ACP and ANP address runtime sessions and decentralized networks. Treat these as contracts for interoperability, not as a reason to invent a private protocol for every deployment.

State is where production systems fail quietly. Session state tracks the current task. Shared memory lets agents build on each other and also creates leakage, staleness, and contradiction risks. Checkpointing lets you resume after a crash. Prefer isolation and explicit conflict rules over optimistic sharing.

Use orchestration when the work needs distinct capabilities, parallel capacity, or failure isolation. Skip it when one agent with a clear tool set already finishes the job. Read the Intro for patterns and protocols. Use the Cheatsheet for the delegation and anti-pattern map.