Why this matters
Without observability, teams learn about quality regressions from customers first. That is too late.
Recommended approach
Capture request IDs, prompt versions, model/provider, token usage, latency, and outcome labels. Add alerting on anomalies before launch traffic ramps.
Implementation checklist
- Trace each user request end-to-end
- Version prompts and route config
- Record structured outputs and validation failures
- Set alert thresholds for cost, latency, and error spikes
Metrics to track
- p95 latency
- Cost per request
- Validation failure rate
- Provider/model error rate
Key takeaway
Observability is the control surface that keeps AI features stable as usage and complexity grow.