Human-in-the-loop is a workflow arrangement in which a person provides input, review, or a decision at a defined point that can influence an AI system's work.
This operational definition concerns the person's role in the workflow. Participation can clarify an instruction, correct a proposed result, or authorize an operation. Those contributions are different: supplying information does not automatically grant permission, and approval does not prove the approved operation occurred.
Identify the contribution the workflow needs
A person may be asked to resolve an ambiguity, assess quality, choose among alternatives, or approve an exception. Name that contribution and the stage at which it can affect the work. A reviewer assessing a draft before release has a different opportunity to intervene from someone reviewing a record after release.
The NIST AI RMF Playbook, GOVERN 3.2 calls for distinguishing human roles and responsibilities, including people using AI systems and those overseeing them. The fact that someone initiated a task does not establish that they have authority over every decision arising from it.
Approval needs authority, information, and scope
When participation is an approval control, identify what the person may decide and what they need to know. The operation, target, relevant inputs, and applicable conditions should be understandable enough for the decision requested. A question that the reviewer cannot resolve should remain unresolved or reach someone able to address it.
Approval may cover one operation or a bounded class of work under stated conditions. It should not transfer silently to a changed destination or task. The workflow also needs to define whether the operation waits, what happens when the reviewer is unavailable, and how the executor applies the decision.
The pre-execution governance guide explains that connection to an operation whose protected effect can still be withheld. Recording the human response is evidence of the response; it is not sufficient evidence that execution respected it.
What the label does not guarantee
Human-in-the-loop does not require a person to approve every agent action. Nor does a confirmation dialog establish informed judgment, effective enforcement, or complete governance. The value depends on the role and the control actually implemented.
The human-in-the-loop governance analysis distinguishes an authorized decision to accept risk from verification of an unknown fact.
The AI agent governance pillar places human participation alongside delegated authority, policy, automated controls, observation, and evidence.