Agent Orchestration
Agent orchestration is the plan that coordinates AI agents, tools, handoffs, shared information, and stopping rules. It keeps a many-step job under control.
Another example
A research job is split into checking sources, writing a summary, and final review. Each worker has a clear limit and sends evidence to one responsible coordinator. Starting many agents with no owner or stop rule is only parallel work, not good orchestration.
Main parts
- 01routing rule
- 02state handoff
- 03termination condition
Use it when
Use this term when several agents or tools need clear routing, owners, handoffs, and stopping rules.
Do not use it when
Do not add several agents when one fixed workflow is simpler and easier to check.
Name used in code
route → handoff → aggregate → stop Before you ship
Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback.
Outcome: Use or evaluate Agent Orchestration to make AI behavior measurable and tied to evidence. User context: A research job is split into checking sources, writing a summary, and final review. Each worker has a clear limit and sends evidence to one responsible coordinator. Starting many agents with no owner or stop rule is only parallel work, not good orchestration. AI method or concept: Agent Orchestration. Why it fits: Use this term when several agents or tools need clear routing, owners, handoffs, and stopping rules. Do not use it when: Do not add several agents when one fixed workflow is simpler and easier to check. AI requirements: Define the input and source evidence. Set model and tool permissions. Use versioned evaluation data and measures. Define failure, decline-to-answer, privacy, speed, and cost limits. Operational safeguards: Keep a clear trace and hide sensitive log data. Show users a safe fallback. Mark steps that need human review. Define how to roll back the model or prompt. Acceptance criteria: Record baseline and target measures on a versioned evaluation set. Test edge cases and hostile inputs. Verify fallback, monitoring, permissions, and rollback. Evidence and limits (evidence boundary): It is official for that source. Other platforms or teams may use the term in another way. Unknowns to confirm: Target task, model and version, evaluation owner, source data, risk limit, tool permissions, and production fallback.
Check this request
Review the current use of Agent Orchestration. Definition: Agent orchestration is the plan that coordinates AI agents, tools, handoffs, shared information, and stopping rules. It keeps a many-step job under control. Release checks: Check the model and prompt versions, source data, versioned evaluation set, and measures. Verify links to evidence, tool permissions, privacy, common failures, decline-to-answer and fallback behavior, human review, monitoring, cost, speed, and rollback. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.