In one sentence
A design where a person reviews, approves or corrects an automated or AI step before its result takes effect.
Human in the loop means a named person checks an automated step at a set point before the result goes out or changes a record. The person might approve a drafted email, confirm a refund, review a claim appeal, or correct a data match. The automation does the gathering and drafting. The person makes the call that carries risk.
Where the check sits
matters more than whether one exists. A check before a message is sent stops a bad message. A check after the fact only finds it. Good designs put the review where a mistake would be costly or hard to undo: payments, clinical or legal statements, deletions, and anything sent to a customer for the first time.
Review fatigue
is the common failure. If a person approves hundreds of near-identical items a day, approvals become a click without reading. Designs should show the reviewer only what changed, highlight low-confidence items, and send routine, well-tested items straight through once the error rate is known to be low.
Frameworks agree on the principle
The NIST AI Risk Management Framework asks organizations to define human oversight roles and responsibilities. OWASP's guidance on excessive agency lists requiring human approval for high-impact actions as a key control. Neither gives a fixed rule for which steps need a person; that depends on the task and the harm a mistake could cause.
In our builds
we write down every step, mark which ones an agent may do alone and which need approval, and log who approved what and when. The approval step is part of the workflow itself, so it cannot be skipped by accident. As results come in, we move steps from reviewed to automatic only when the numbers support it, and we keep a single off switch that pauses the whole system.
Keep following the system
AI Agent Governance
The rules, roles, limits and records that decide what an AI agent may do, who answers for it and how it is checked.
Excessive Agency
The risk that an AI system can take damaging actions because it has more functions, permissions or autonomy than its task needs.
AI Agent
A system that uses a model and tools to pursue a goal across multiple steps within defined permissions and review boundaries.
Agentic Workflow
A workflow where an AI model decides some of the steps at run time, inside limits set by the people who built it.
Prompt Injection
An attack where text in an input tricks an AI model into ignoring its instructions or taking actions it should not.