AI governance ·
How should institutions govern agentic AI?
Agentic AI governance is the set of controls for AI that acts — systems that take multi-step actions toward a goal, use tools, and make intermediate decisions — rather than only producing an answer for a human to act on. Governing it adds three things to ordinary AI governance: control over authority, boundaries, and reversibility.
The line that changes everything: advising vs. acting
A chatbot produces an output; a human decides whether to act on it. An agent acts. That single shift moves the risk from "was the answer accurate?" to "what did the system do, on whose authority, and can we undo it?" Most institutions still govern AI as if it only advises. The moment an agent can send, transact, schedule, or change a record, governance built for advice is no longer enough.
The four controls agentic AI requires
- 1. Authority. An explicit grant of what the agent may do, scoped as narrowly as the task allows — never "general access because it is convenient."
- 2. Boundaries. Hard limits the agent cannot cross regardless of its goal: actions it may never take, thresholds above which it must stop, data it may never touch.
- 3. Reversibility. Every consequential action logged, attributable, and — wherever possible — reversible, with a defined path to catch and undo a wrong action.
- 4. Human checkpoints. Named points where a person must approve before the action takes effect, calibrated to the stakes rather than applied uniformly.
An agentic-AI risk checklist for boards
| Question | If the answer is no… |
|---|---|
| Do we know exactly what each agent is authorized to do? | Scope of exposure is unknown |
| Are there hard limits it cannot cross? | A goal can justify a harmful action |
| Can we see and reverse what it did? | Errors compound silently |
| Does a human approve high-stakes actions? | Accountability has no anchor |
Verify before you deploy
Because an agent acts, the cost of a wrong behavior is realized, not hypothetical. That makes adversarial verification before deployment essential rather than optional: stress-testing the agent across many lenses to find where it exceeds its authority, crosses a boundary, or takes an irreversible action under pressure — while it is still cheap to fix. This is exactly what my verification method is built to do, and it is the difference between an agent that clears governance and one that fails in production.
Key takeaways. Agentic AI moves the risk from answers to actions. Govern authority, boundaries, and reversibility, with human checkpoints scaled to the stakes. And verify behavior adversarially before deployment, because with agents the failure is real.