AI Agent Governance: Controls You Can Verify
AI agent governance is the practice of assigning responsibility and controlling how agents access data, invoke tools, and release results. A detector setting alone is not governance: the organization also needs a defined operating boundary, application enforcement, evidence, and a process for reviewing changes.
The NIST AI Risk Management Framework offers a voluntary structure for organizing risk work. The engineering workflow below focuses on controls a team can inspect for one agent. It is not a certification or a complete organizational governance program.
Name an owner and a permitted workflow
Document the agent's purpose, accountable team, approved users, connected models, retrieval sources, and tools. Identify who can change its configuration and who reviews an incident or a failed release check.
Make the permission boundary specific. “The support agent may read public product documentation” is more testable than “the agent follows company policy.” Account access and authorization should be enforced by application controls, not inferred from a model's willingness to comply.
Distinguish evaluation from enforcement
An evaluation returns information about the configured checks. The application must decide whether to continue. For direct SDK or API use, verify completion, errors, required policy execution, and the returned decision before the protected operation.
A warning policy can record a finding while allowing an operation. A blocking policy needs an integration that actually stops at the intended point. Read policy behavior and enforcement scope before treating a dashboard badge as evidence of prevention.
Treat input, output, and tools as separate boundaries
An input check runs before the next model operation. It does not establish that the model's eventual response is acceptable.
An output-release gate needs access to the generated content before releasing it. A buffered response can be evaluated before delivery; streaming designs need an explicit buffering or release strategy.
A tool boundary must validate the requested operation and its parameters in the relevant user context. General prompt screening does not replace tool authorization.
Choose how incomplete checks behave
A policy can be configured while its detector or model endpoint is unavailable. Exercise timeouts and failure paths, and decide how your application behaves when a required check cannot finish.
Keep “not evaluated,” “incomplete,” and “completed with no finding” distinct in monitoring and release decisions. Review the support matrix for the operations and versions that have actually been verified.
Connect operational evidence to changes
Recorded traces can connect a request to spans, policy decisions, and findings. They cover instrumented activity; they do not prove that every model or tool operation was captured.
Maintain a repeatable case for important failures. Retest after changes to the model, prompt, retrieval source, tools, or policies, and compare the same assertion scope. See traces and findings and regressions in CI.
Review processing, capture, and retention separately
Inventory where evaluation text is processed and which destinations are approved. Then decide what telemetry content may be captured, who can access it, and how long records should remain.
Metadata-only storage does not mean text stayed inside the application. A private installation also needs validation of identity, model endpoints, backups, workers, and operating ownership. Start with data handling and private deployment operations.
Start with a measurable acceptance check
For one agent, record its owner, allowed actions, failure behavior, tested assertions, and evidence links. Run a benign request, a known synthetic failure, and an unavailable-check scenario. Review the actual application behavior before expanding the rollout.