Human review is most effective when it is designed into the process rather than added after an AI system makes a mistake. The workflow should explain which cases need review, what information the reviewer sees, and how the system continues after a decision.
In multilingual feedback routing, an uncertain classification may need attention before a ticket is sent to a team. The same principle applies when model output influences another customer-facing or operational action.
Choose review triggers
Possible triggers include missing required information, an unsupported language, an ambiguous category, disagreement between validation rules, or an action with significant consequences. Do not depend on a model's confidence value alone unless it has been calibrated and evaluated for the specific task.
Give reviewers useful context
Show the original input or a protected reference, the proposed result, the relevant rule, and the reason the workflow paused. Let the reviewer correct or reject the suggestion. Avoid presenting a model-generated answer as an established fact.
Define what happens after review
Record the reviewer's decision and resume from a known workflow state. If the case cannot be resolved, make the next owner explicit. Use stable identifiers so a retry or reviewer action does not create duplicate tickets or notifications.
Measure cases sent for review, time awaiting a decision, common correction reasons, and unresolved cases. Do not treat a low review rate as success unless automated outcomes are also checked. The [BMRC feedback-routing case study](/case-studies/bmrc-hospital-ai-feedback-agent/) describes its operational scope.