Your next AI problem doesn't score. It acts.
Predictive models suggest; someone accepts or ignores them. The wave now arriving — scheduling agents, prior-authorization bots, ambient scribes that file orders — acts. A model that degrades produces a bad number. An agent that degrades produces a bad action, already taken. Governance built for models does not survive that difference.
The framework in one paragraph
Healthcare already owns the right governing instrument, and has run it for a century: privileging. No clinician acts in a hospital without a defined scope of practice, a proctored onboarding period, ongoing peer review, and a revocation pathway. The Agent Privileging Framework applies exactly that machinery to software that acts: a written scope of action, a proctoring period before autonomy, action-level audit against that scope, automatic suspension on any out-of-scope action, and a revocation and reinstatement record with a named human on every decision.
Why now, before any standard requires it
As of this writing, no certification program speaks operationally to agentic AI — The Joint Commission's Responsible Use of AI in Healthcare (RUAIH) certification and the Coalition for Health AI (CHAI) frameworks address AI tools broadly, and agent-specific standards do not yet exist. That is precisely the argument for starting: governance evidence is dated, and dates cannot be backdated. Organizations that begin privileging their agents now will hold years of accumulated record when standards arrive; organizations that wait will hold a policy binder written the month before the survey. The same asymmetry already played out with model governance.
The five components
- Scope of action — the finite, written list of things an agent may do. Everything else is a breach by definition.
- Proctoring — the supervised period between deployment and privileges, with explicit graduation criteria.
- Action-level audit — every action logged against scope; the log, not the vendor's assurance, is the evidence.
- Human override — who can halt an agent, how fast, and how the halt itself is recorded.
- Suspension, revocation & reinstatement — the pathway with teeth, including automatic suspension on out-of-scope action.
The framework, running as software
HAI-OS implements this framework natively: an agent registry with scopes and proctoring clocks, an action log that computes scope compliance at write time, automatic suspension on breach, and every transition written to the same tamper-evident audit chain as model governance — one spine for everything that acts in the organization. See it in the public demo, or read the platform overview.
The Agent Privileging Framework is published by the Healthcare AI Institute as an independent operational framework. It is not affiliated with or endorsed by The Joint Commission, CHAI, or any certifying body.