Human-on-the-Loop (HOTL)
Also called: HOTL
Human-on-the-loop is a supervision model in which a person monitors an AI system's overall behaviour and intervenes only when a result crosses a defined threshold or is flagged as an exception, rather than reviewing every individual action. It is a step up in autonomy from human-in-the-loop, where a person approves each output before it takes effect. As agents earn more trust, oversight typically moves from gating every task to monitoring the portfolio of tasks an agent is running.
In practice
An agent processing expense claims might act automatically on anything under a set value and route the rest to a person for sign-off. Moving from in-the-loop to on-the-loop is where the labour saving actually appears, so the governance question is what evidence justifies raising the threshold.