AI observability is the practice of using telemetry to understand the behavior and operation of AI systems, including models, applications, and the workflows that connect them.
This operational definition is broader than agent tracing. A system that classifies a document or generates a draft can have observability needs without delegating a sequence of actions to an agent. The relevant scope depends on the system and the question being investigated.
Identify what is being observed
Observations can concern a model invocation, application processing, data movement, infrastructure behavior, or a user-facing result. A model response time and the time until an application makes its result available describe different parts of a workflow. Name the boundary before comparing their measurements.
The OpenTelemetry observability primer connects understanding system behavior to instrumentation that emits signals such as traces, metrics, and logs. Those signals are means of observation, not a universal schema for AI behavior or a guarantee that the application provides all necessary context.
Useful context may include the component, configuration, operation, and source of an observation. Determine which details are required to explain the behavior under review. An aggregated measurement can reveal a pattern while leaving the individual interaction needed for investigation unidentified.
Collection does not settle interpretation
A response arriving within an expected time does not establish that its content is suitable for the task. Assessing that suitability requires relevant criteria and evidence, not just a successful transport status. Operational observations can inform evaluation without replacing it.
Likewise, an application log may describe only the interactions that passed through its own path. It does not establish a complete inventory of enterprise AI use or explain activity through another client. Collection gaps should remain visible in the resulting assessment.
Protect the observation data according to what it contains. Full prompts or outputs can introduce another copy of sensitive information; collect the minimum detail that supports the question and control access to any retained content. Observability does not remove those handling responsibilities.
Related concepts
Agent observability concentrates on an agent's work and interactions. It is a particular observability scope, while AI observability can also cover systems that do not act as agents.
AI usage governance determines permitted purposes and conditions for using AI. Observations can help verify those conditions, but a record of activity does not establish its authorization or the effectiveness of a control.
The enterprise AI security pillar connects evidence across human usage, applications, and agent workflows to the resources and owners involved.