Agent orchestration
The control layer that coordinates multiple agents or agent steps — routing work, managing state, enforcing hand-off rules, and resolving conflicts between specialized agents.
Where multi-agent describes the architecture (several agents collaborating), orchestration describes the control plane (how they actually coordinate). The orchestrator decides which agent handles a request, what state gets passed between them, how to detect when an agent is stuck, and how to recover.
Common orchestration patterns in 2026: hierarchical (a planner delegates to workers), pipeline (agents run in sequence, each refining the previous output), router (a classifier dispatches to specialists), and swarm (peer agents collaborate with no central coordinator). Hierarchical wins most production deployments — it is the most observable and debuggable.
Orchestration frameworks: LangGraph for stateful graph-based orchestration, CrewAI for role-based crews, Anthropic's Agent SDK for vendor-native coordination, and increasingly custom-built orchestrators when off-the-shelf does not fit. The right choice depends on how stateful your flows are and how much observability you need.
Frequently asked
When do I need agent orchestration?+
When a single agent hits a complexity ceiling — too many tools, too long a context, or tasks that genuinely benefit from specialized sub-agents. Otherwise, a well-tuned single agent is simpler and cheaper.
Is agent orchestration the same as multi-agent?+
Multi-agent is the system; orchestration is the control plane. Every multi-agent system needs orchestration; not every orchestration involves multiple LLM agents (workflow engines orchestrate a single agent across steps).