AI agent governance framework

AI agent governance that changes what the agent can actually do

Turn AI agent policy into enforceable identity, permissions, approval gates, evaluations, trace evidence, ownership, and incident controls.

Answer in brief

AI agent governance is effective when policy becomes runtime control. Each agent needs an owner, identity, permitted data, scoped tools, approval rules, evaluation evidence, trace retention, and a process for exceptions, incidents, and retirement.

Govern the action boundary

A policy document cannot stop a tool call. Enforce scope at the credential, connector, tool, and workflow layers. Separate read from write operations and make high-impact actions explicit nodes that can pause for approval.

Assign accountability

Every production agent needs a business owner for the outcome and a technical owner for the runtime. Record who approved the use case, which evaluation set supports release, who reviews exceptions, and when the approval expires.

  • Inventory and risk classification
  • Named owners and reviewers
  • Versioned approval and evaluation evidence
  • Periodic access and performance review
  • Retirement and data-deletion procedure

Measure outcomes and exceptions

Track task success, correction rate, approval rejection, policy violations, tool errors, cost, and latency. Review failure clusters rather than averaging them away. A 98 percent success rate can still be unacceptable if the remaining two percent are payments or customer-facing writes.

Frequently asked questions

Who should own AI agent governance?

Ownership is shared. The business owner owns the outcome and acceptable risk; engineering owns runtime controls; security, privacy, and legal set applicable constraints.

Is logging enough for governance?

No. Logs provide evidence after an event. Governance also needs preventive permissions, approvals, testing, change control, and a way to stop execution.

How often should an agent be reviewed?

Review frequency should follow risk and change rate. High-impact agents need review after material model, tool, prompt, data, or policy changes and on a regular schedule.

Last reviewed 20 August 2026 · Current product scope: Android, not iOS
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