Human-in-the-Loop (HITL)

A governance and safety mechanism in AI systems where humans review, approve, or override model outputs before final decisions or actions are taken. In Agentic AI, HITL ensures accountability by inserting subject matter experts or operators into critical steps—such as model approvals, sensitive recommendations, or exception handling—to reduce risk and increase trust in autonomous systems.