Why AI Observability Matters as Systems Scale

Traditional monitoring tracks uptime and response times, but AI systems also need visibility into answer quality, which is much harder to measure automatically.

We track proxy metrics like retrieval relevance scores, user feedback signals such as thumbs up or down, and flag responses that fall outside expected patterns for human review.

Without this kind of observability, a gradual decline in AI answer quality can go unnoticed for weeks, since traditional uptime metrics will look completely normal.