// glossary
Agent Orchestration plain-English.
Operator-grade definition. Plain words, plus where the term shows up in real work.
What is a Agent Orchestration?
Agent orchestration is the coordination layer that lets multiple AI agents share work, avoid collisions, and produce a single coherent outcome. It covers task dispatch, lane or queue management, handoffs, contracts between agents, and the operator authority that supervises all of it.
A single agent can be useful. A swarm of agents without coordination is mostly noise — duplicate work, conflicting writes, deadlocks on shared state. Orchestration is what turns the swarm from a demo into a production fleet that ships measurable work without one human babysitting every step.
Common shapes: a handoff queue (one author at a time, simple), a fleet broker (routes by skill, parallel), a lane-claim system (parallel writers each owning a lane, no collisions), and a contract-publish pattern (long-lived integrations versioned through a typed contract). Each trades latency, isolation, and replayability differently.
Underneath every healthy orchestration shape is the same discipline: explicit scopes, explicit handoffs, an audit trail, and an operator with kill-switch authority. The protocol is easy; the operator discipline is what keeps the system honest.
Related questions
Do I always need orchestration with multiple agents?
If they share any state — code, data, decisions — yes. Without orchestration the cheapest failure mode is silent corruption of that shared state, which is expensive to debug.
What is the simplest orchestration shape?
A handoff queue: one author at a time, work moves through the queue in order. Easy to reason about; ceiling is single-agent throughput.
How is orchestration different from a workflow engine?
Overlapping. Workflow engines (Temporal, Airflow) handle durable state and retries. Agent orchestration adds AI-specific affordances: tool budgets, eval gates, model routing, operator authority over autonomous actors.
Related work
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