AI Agent Orchestration
AI agent orchestration is the coordination of multiple agents — or one agent across many steps — so that work flows between them: who runs next, what context passes along, and what happens when a step fails. Orchestration arranges the work; governance constrains it.
What AI agent orchestration means
As enterprises move from single agents to chains, orchestration becomes the load-bearing layer: a supervisor routing tasks to specialist agents, sequential pipelines passing state, or agents negotiating over shared work. It introduces governance problems that single-agent deployments never had. One agent's output becomes another's input, so errors compound. Chains create agency chains — when agent B takes a bad action on agent A's instruction, attribution and approval must survive the hop. And the orchestration layer itself makes decisions (routing, retries, escalation) that deserve the same scrutiny as agent decisions. Governance for orchestrated systems therefore checks calls at every hop — each agent keeps its own identity, its token capped to the caller's rights, so authority never widens along the chain — and the orchestration state machine leaves records like any other component. Orchestrating without governance produces systems whose failures are multiplicative and whose audit trails dead-end at "the coordinator decided."
Frequently asked questions
What is AI agent orchestration?
The coordination layer that routes work between agents or across an agent's steps: sequencing, context passing, failure handling. Frameworks like LangGraph implement it; the governance question is what checks apply at each hop.
What new risks does orchestrating multiple agents create?
Error compounding (one agent's output becomes the next's input), authority widening (an agent chain can accumulate permissions no single agent had), and audit dead-ends (decisions made by the coordinator, not attributable to an accountable agent). Per-agent identity with capped delegation is the fix for all three.