Human-in-the-Loop
A design pattern where a human reviews, approves, or corrects AI decisions at specific points in a workflow — concentrated where judgment matters, not applied uniformly.
Production AI is not a prompt. It is a system of context, tools, permissions, traces, evals, and feedback loops.
What it is
Human-in-the-loop (HITL) is the practice of designing AI workflows so that humans intervene at chosen points instead of everywhere. The goal is to put human judgment where it actually adds value — high-impact decisions, low-confidence outputs, edge cases the system has not seen — and let the agent handle everything else.
Why it matters
'Review every AI output' is the lazy form of HITL and it collapses under volume. 'No human review' is the dangerous form and it gets caught by an avoidable failure. Good HITL design is the discipline of picking the right inflection points and routing them to a queue that scales.
How it works
Approval gates on high-risk actions; low-confidence escalation when the model signals uncertainty; sampling-based review on auto-resolved cases; correction capture that feeds rejected outputs back into the eval set. The combination produces a system that gets quieter over time as the eval set grows.
Related resources
Approval gates that put a human in the loop where correctness, risk, or accountability actually require human judgment — designed as part of the workflow, not as a panic button bolted on after launch.
A point in an AI workflow where an action is suspended until a human reviews and approves, rejects, or modifies it.
A capability in the Group e-media information AI stack. This resource connects the subject to data substrate, agent runtime, evals, and operations.
A capability in the Group e-media information AI stack. This resource connects the subject to data substrate, agent runtime, evals, and operations.
A capability in the Group e-media information AI stack. This resource connects the subject to data substrate, agent runtime, evals, and operations.