Governing Agentic AI Before It Reaches Runtime

An AI agent is more than a model that returns text. It can choose a tool, call a service, update a record, or hand work to another agent. That changes the governance question from “Is this model approved?” to “What is this agent allowed to do, for whom, under which conditions, and how would we know if it went wrong?”

That question needs an answer before deployment. A policy document alone cannot supply it.

Start with the job, not the agent label

“Agentic” describes a range of designs. One assistant may only draft an email; another may read customer records, write to a CRM, and schedule follow-up actions. Treating both as the same risk category hides the differences that matter.

Start with a specific workflow. Record the business owner, the people affected, the data involved, the tools the agent can reach, and the decisions it can make without review. Then describe the expected benefit and the conditions under which the system should stop.

The NIST AI Risk Management Framework is useful here because its Govern, Map, Measure, and Manage functions are designed to work across an AI system’s lifecycle. Governance is cross-cutting: it informs context mapping, measurement, and ongoing risk treatment. NIST also describes the framework as voluntary and says its functions are not a prescribed checklist.

Treat identity and permissions as part of governance

An agent needs a distinct identity in the systems it accesses. If every agent call appears as a shared administrator or as the human who first opened a chat, investigators cannot reliably tell which actor initiated a change.

For each agent, define an owner, environment, purpose, allowed tools, data boundary, and delegation rule. Give it only the permissions needed for its current task. Require a person to approve consequential actions such as payments, external communications, or changes to access controls. Log the human request, agent identity, tool call, result, and approval decision together.

Palo Alto Networks’ Securing Agentic AI whitepaper describes identity, governance, and data protection as foundations for securing autonomous AI. Its Prisma AIRS 3.0 announcement also highlights agent identity and runtime controls, including an AI Agent Gateway described there as being in limited preview. These are useful signals about the security problems vendors are addressing. They are vendor materials, so treat their product claims as one input to your architecture and verify availability and fit independently.

Put a runtime boundary around each action

Approval at the beginning of a project is not a substitute for controls during operation. An agent can behave differently when its tools, prompts, retrieved information, or environment change.

A practical runtime boundary answers four questions:

1. Can this agent call this tool? Check the agent’s identity and task scope at the point of access.
2. Can it use this data? Enforce data permissions where the data is stored or served, not only in a system prompt.
3. Does this action need a person? Route higher-impact actions to an explicit approval step.
4. Can the action be reconstructed or stopped? Keep useful event records, set limits, and provide a way to revoke access or disable the agent.

The control should match the consequence. A low-risk internal summary may need monitoring and clear ownership. An agent that can change customer entitlements needs tighter access, stronger validation, and a human decision point.

Measure operating behavior, not just model quality

Model benchmarks can help compare models, but they do not answer whether a workflow is safe or valuable. Track workflow measures such as task completion, error and rework rates, tool-call failures, policy denials, approvals, escalation rates, and cost per completed task. Decide who reviews those measures and how they trigger a change in scope.

The NIST AI RMF Playbook offers suggested actions aligned with the framework’s functions. NIST explicitly frames the playbook as adaptable support, not a universal checklist. That flexibility is helpful: the same governance intent can be applied differently to a drafting assistant and an agent that changes financial records.

Keep the deployment decision reversible

A useful approval is conditional. It names the workflow, owner, authorized tools, affected users, measurement plan, and review date. It also says what would cause the team to pause or roll back the deployment.

That turns AI governance into an operating agreement between the business, engineering, security, and risk teams. The agreement can evolve as the agent gains new tools or authority. If the scope changes materially, the review changes with it.

Agentic AI does not make governance obsolete. It makes the links between intent, identity, permission, action, and evidence harder to leave implicit.