Production Monitoring & Drift Management

AI feature quality can degrade after release due to data drift, behavior drift, or changing user patterns. Monitoring MUST detect this early.

Telemetry Requirements

Minimum production telemetry:

• Request volume and success/error rates
• Latency percentiles and timeout rates
• Quality proxy metrics (acceptance, correction, escalation)
• Safety incidents and policy violations
• Segment-level performance for critical cohorts

Drift Types

Drift Type	Signal	Response
Data drift	Input distribution changes materially	Re-evaluate model on fresh sample
Concept drift	Target behavior or business rules changed	Update rubric/model/prompt and revalidate
Behavioral drift	Output style/quality degrades without infra failure	Trigger incident triage and rollback decision

Alert Policy

• Define warning and critical thresholds per metric.
• Critical alerts MUST route to on-call owner within 5 minutes.
• Repeated warnings over 7 days SHOULD trigger formal review.

Drift Response Runbook

1. Detect and classify drift severity.
2. Evaluate blast radius (users, revenue, compliance exposure).
3. Apply mitigation: rollback, feature-flag reduction, or safe-mode fallback.
4. Re-evaluate candidate fix against current data.
5. Publish incident note and corrective action.

Monitoring Ownership

Responsibility	Owner
Metric definition and SLOs	Product + Engineering
Dashboards and alerts	Platform/Observability
Drift triage	AI feature on-call
Governance reporting	CoE + Security