Monitoring
Monitoring shows whether deployed AI systems are behaving acceptably after they go live.
Orlo monitoring is focused on the AI control plane. It complements infrastructure logging and tracing; it does not replace them.
Who Uses It
- platform and ML teams watching runtime behavior
- reliability teams checking operational health
- risk teams looking for review pressure or drift
- domain owners watching failure patterns
What It Shows
Monitoring can surface:
- validation failure rates
- inference volume and latency
- routing and fallback behavior
- circuit-breaker state
- retrieval or attribution issues
- judge drift and evaluation drift signals
- feedback volume and correction patterns
- task or deployment health
Why It Matters
An AI system can pass a launch evaluation and still degrade in production.
Monitoring helps teams see when production evidence no longer matches the launch assumption.