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Guide

AI Agent Orchestration Control Plane

A practical framework for evaluating orchestration reliability and governance readiness in production.

Core Capabilities

What to evaluate first

Choose control-plane features by production risk, not demo convenience.

Workflow orchestration

DAG execution, retries, re-runs, and deterministic state transitions.

Policy and approvals

Pre-dispatch controls and risk-tiered approval workflows for high-impact actions.

Reliability controls

Backpressure, worker routing, reconciliation, and failure recovery playbooks.

Checklist

Production evaluation checklist

Use this scorecard before selecting orchestration and governance tooling.

  • Confirm orchestration behavior under retries, worker loss, and partial outages.
  • Require policy decisions before execution for high-risk actions.
  • Add approval workflows for production writes and external-impact actions.
  • Validate run-level audit evidence for every decision and transition.
  • Score vendor/stack options with the same checklist in staging before rollout.

Frequently Asked Questions

What is an AI agent control plane?
An AI agent control plane is the governance and operations layer that manages orchestration, policy decisions, approvals, and audit evidence across autonomous agent workflows.
How is orchestration different from governance?
Orchestration coordinates task execution and state transitions. Governance decides what actions are allowed, when approval is required, and how evidence is captured.
Do I need both orchestration and a control plane?
For production autonomous agents, yes. Orchestration alone handles flow mechanics, but a control plane adds preventive safety and accountability controls.
Which capability is most important first?
Pre-dispatch policy enforcement is usually the highest-leverage first capability because it prevents unsafe side effects before they happen.