Definition: AI observability is the ability to see what an AI system is doing in production, by tracing each request through prompts, retrieval, model calls and tool actions, and measuring quality, cost and latency over time.
Each request produces a trace: the input, the retrieved documents, the prompt sent, the model and version used, any tool calls, the final output and the time and tokens spent at each step. Dashboards and alerts sit on top, and samples of traces are scored for quality, either automatically or by reviewers.
Traditional monitoring shows whether a service is up. AI observability shows whether answers are still good. It helps teams find the step that caused a wrong answer, catch model drift, control spend and produce the records that AI governance reviews ask for.
Teams instrument applications with tracing libraries or OpenTelemetry, feed real failures back into evaluation sets, and review traces when a prompt, model or data source changes. It is a core part of LLMOps.
Ryz MLOps engineers and site reliability engineers instrument AI systems in your cloud, working on your team.
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