Approval Queues Emerge as the Key Human-AI Handoff Layer in Agentic Systems

Enterprise AI agents increasingly rely on structured approval queues to pause execution and hand control to human reviewers before performing high-risk actions such as sending emails, updating records, or issuing refunds. Unlike simple chat-based oversight, these queues require each review item to carry the proposed action, risk reasoning, owner details, allowed decisions, escalation paths, and a timeout. Frameworks like LangGraph implement this through an interrupt model that saves full agent state, waits indefinitely for human input, and resumes execution on the same thread once a decision is made. OpenAI's Agents SDK follows a comparable approach, treating human-in-the-loop as a structured tool-call policy rather than an informal checkpoint. The shift reframes 'human in the loop' from a vague design principle into a precise runtime boundary that governs when and how autonomous agents yield control.