AI-generated code needs extra monitoring in production:
- Error rates by endpoint (AI code may have hidden bugs)
- Performance metrics (latency percentiles, memory usage)
- Business metrics (does the feature work as intended?)
- Anomaly detection (catch issues before users report them)
AI code passes tests but may behave unexpectedly under real load. Monitoring catches what tests miss.