How does agent governance relate to an audit trail?
An audit trail is one of the concrete outputs agent governance typically requires — a record of what an agent did, when, and under what authorization, which supports both internal accountability and the kind of documented oversight regulators increasingly expect for autonomous, multi-step AI systems.
Related terms
Audit Trail
The recorded history of what an AI system did, when, with what data, and under whose authorization — the evidence an organization actually needs the moment a regulator, customer, or internal investigation asks "prove it."
Hallucination (AI Hallucination)
An AI model generating output that's fluent, confident, and entirely wrong — not a bug that occasionally slips through, but a structural property of how these models work, which means the real question isn't whether hallucination happens, but what catches it before someone acts on it.
HIPAA (Health Insurance Portability and Accountability Act)
The US law governing how protected health information can be used, stored, and shared — and one of the clearest examples of a regulation written decades before AI existed that now has to be applied, without modification, to AI tools its drafters never anticipated.
How is AI agent governance different from AI governance generally?
AI Governance covers policies and oversight across an organization's entire AI footprint. AI agent governance is a narrower, more specific discipline focused on the particular risks agents introduce — autonomous decision-making, tool use, and chained multi-step actions — that general AI policies weren't originally built to address.
Human-in-the-Loop
The requirement that a person review, approve, or be able to override an AI system's output before it becomes a real decision — the single control most directly responsible for catching hallucinations, biased outcomes, and consequential errors before they reach the person they affect.
See How does agent governance relate to an audit trail? in practice
Questa AI anonymizes sensitive data before it reaches any AI model — across documents and live prompts, with governance and data-residency control.