How to Detect When an AI Assistant Starts Misclassifying Customer Intent

An assistant may route new customer requests to the wrong category after model, prompt, or product changes. Aggregate resolution rates can hide errors concentrated in a specific intent.

What to check

Review misrouted examples, confidence and fallback thresholds, recent taxonomy changes, language and channel segments, and downstream queue outcomes. Use privacy-safe samples and compare with human labels.

Recommended next step

Update evaluation examples and routing thresholds with service owners, then stage changes and monitor by intent. Provide a clear human correction path and record corrected classifications for future review.

How Informatix Systems can help

Informatix Systems provides AI production operations and workflow evaluation services. We can assess the affected environment, narrow down the failure, and help plan a practical correction. Contact us with the system involved, when the issue began, and non-sensitive error details.