Human in the loop
Human in the loop is designing an AI system so a person reviews, approves or edits the key steps before they take effect. It is the honest answer to how you make an agentic system safe enough to ship: you keep a human on the high-stakes decisions and automate the cheap ones.
A human-in-the-loop design treats the model as a draftsperson, not a decision-maker. An AI agent drafts the refund, classifies the support ticket or proposes the SQL change, and a person clicks approve before anything reaches the customer or the database. The interface — a queue, a diff view, a single Slack button — is part of the product, not an afterthought.
The trade-off is throughput. More oversight means slower decisions and a real human cost per action, which is why teams are tempted to skip it. The honest take is that a fully autonomous agentic workflow on production data is rarely worth the risk early on, because hallucinations and bad tool calls compound faster than they get caught.
The rule of thumb is to start with humans on every step, instrument the system with evals and MLOps telemetry, then peel humans off the steps where the model is demonstrably right often enough. Low-stakes, reversible actions go first; anything that touches money, identity or external communication stays gated for a long time. Done well, human-in-the-loop is not a stopgap until the model gets better — it is the operating model that lets you ship today and earn autonomy step by step.
