// glossary
Agent Orchestration plain-English.
Operator-grade definition. Plain words, plus where the term shows up in real work.
What Is 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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