Human-in-the-Loop Approval
Human-in-the-loop approval places defined checkpoints where AI agent work pauses for human sign-off before proceeding.
Human-in-the-loop approval is the governance mechanism that places humans at defined checkpoints in agent work: when an agent reaches an action its permissions mark as consequential, it pauses and waits for a person to approve, adjust, or reject before proceeding.
The design question it answers is where autonomy should stop. Requiring approval for everything reduces agents to expensive suggestion boxes; requiring it for nothing hands the organization's judgment to software. Human-in-the-loop approval resolves the trade-off with policy — routine actions flow, defined categories of consequence wait for sign-off, and the boundary between them is explicit and adjustable.
In an AI Autonomous Organization, these approval gates are how governance becomes operational: the rules about what agents may do are enforced at the exact moments that matter, and each decision is captured in the audit trail.
Common questions
Doesn't human approval defeat the point of automation?
Only if it is applied everywhere. The model lets routine work flow autonomously and reserves approval for actions defined as consequential, so oversight lands where the stakes are.
Who decides which actions need approval?
The organization does, through its governance policies. The approval boundary is explicit, set by humans, and adjustable as trust in an agent's track record grows.