Human-in-the-Loop

Human-in-the-loop (HITL) is a design approach in which a person is deliberately placed at a decision point in an automated process, so that the AI proposes and a human approves, edits, or rejects before an action is committed. It is used to keep accountability with a person for consequential steps, to catch errors before they reach customers or systems of record, and to build trust while an automated system is still being evaluated. The human's decisions can also serve as feedback that improves the system over time.

In agentic and AI-workforce products, human-in-the-loop commonly means that agents do the analysis and draft the concrete action, such as an email, a record update, or a code change, but nothing fires until someone clicks to approve it. Systems vary in how much they gate: some require approval on every action, while others let low-risk, well-established actions run automatically and reserve human review for higher-stakes steps. The approach is a middle ground between fully manual work and fully autonomous automation.