Traditional alerts tell you the AI endpoint is slow or unavailable. Quality-aware alerts tell you it is answering badly.
Choose signals tied to named risks: groundedness for a retrieval assistant, tool success for an agent, schema validity for extraction, or safe language for a health-adjacent feature. Segment by version and task so a local regression does not disappear in a global average.
Alert only when the change is sustained and the sample is meaningful. Include representative failed traces, baseline comparison, affected cohort, and deployed versions. A number without examples creates investigation work instead of reducing it.
Every alert needs an owner and response: inspect traces, pause a rollout, route cases to humans, or open an incident. Review false alarms and update thresholds or evaluators.
Quality-aware monitoring is valuable when it changes action before users create the support ticket. Otherwise it is just another dashboard.