A model that scored well in testing can still drift once it meets real traffic: a data source changes format, users start asking questions the system was never tested on, or costs climb without an obvious cause. Observability tools capture every prompt, response, token count and latency figure so a team can see what is actually happening rather than guessing from user complaints.
The limit is that observability tells a team something changed, not always why it changed or whether the new answers are still correct. It works alongside evals, which check correctness against known cases, rather than replacing them. Most teams add observability once an AI feature reaches real users, not during an early proof of concept.