AI Agent Governance
AI agent governance is the practice of enforcing policy, approvals, and audit over the actions autonomous AI agents take — controlling which tools they call, what data they touch, and which side effects they cause in production systems.
Definition
AI agent governance is the practice of enforcing policy, approvals, and audit over the actions autonomous AI agents take — controlling which tools they call, what data they touch, and which side effects they cause in production systems.
What it covers
AI agent governance focuses on agent behavior, not model quality. Where model evaluation asks whether an LLM produced a good answer, governance asks whether the action that answer triggered is permitted: should this agent be allowed to run this command, send this email, or delete this record? It spans policy definition (the rules), policy enforcement (a decision point that checks every action before it executes), human approval routing for high-risk operations, and a tamper-evident audit trail that records every decision. Governance is a runtime control, applied continuously, not a one-time design review.
Why it matters now
Autonomous agents chain many tool calls per task, and a single bad reasoning step can cascade into real-world side effects. Most agent frameworks observe what already happened rather than gate what is about to happen, leaving teams with logs instead of controls. Governance closes that gap by inserting an enforcement boundary between the agent and the systems it acts on, so that policy — not the model's confidence — decides whether a sensitive action proceeds.
Frequently asked questions
How is AI agent governance different from model evaluation?
Model evaluation measures the quality of an LLM's output. AI agent governance controls what the agent is allowed to do with that output — which tools, data, and side effects are permitted. The two are complementary: evaluation improves the answer, governance constrains the action.
Is AI agent governance only an enterprise concern?
No. Any team running agents that can touch production systems — sending messages, modifying infrastructure, or accessing customer data — benefits from a policy and audit boundary. The need scales with the blast radius of the agent's actions, not the size of the company.
Related reading
Govern your AI agents with Cordum
Cordum is the agent control plane: policy-before-dispatch enforcement, human approvals, and a tamper-evident audit trail for autonomous AI agents.