Glossary
AI Observability
The ability to monitor, inspect, and understand what an AI system is doing in production — including inputs, outputs, errors, and behavioral patterns over time.
AI observability adapts software engineering’s observability concept to the particular challenges of AI systems: being able to see what a model is doing in real time, understand why it’s behaving the way it is, and detect problems quickly when they arise. This goes beyond basic logging to include structured tracing of model calls, tracking of behavioral metrics over time, alerts for anomalous behavior, and the infrastructure needed to reproduce and investigate failures. For behavior architects, observability is the operational foundation of behavioral governance: without visibility into what the model is doing in production, you’re managing behavior based on assumptions rather than evidence, and problems can persist for weeks before they’re discovered.