An organization operates a generative AI application in production by using Microsoft Foundry.

The two requirements target fundamentally different stages of the AI application lifecycle. Continuous runtime monitoring addresses production behavior — an application that intermittently times out needs real-time visibility into latency, error rates, and request volumes for every call in production. Tracing backed by Application Insights or OpenTelemetry exporters is correct here because it captures granular, per-request telemetry continuously without requiring manual triggering — it is always-on by design. Release-time validation addresses pre-release quality gates — before new prompts or model updates go live, you need to verify they have not introduced regressions. An evaluation pipeline in Microsoft Foundry ' s prompt flow is correct here: it runs the updated application against a predefined test dataset, scores outputs on quality and safety metrics, and passes or fails the release based on thresholds. Evaluation pipelines run on demand triggered by CI/CD, not continuously.

Microsoft Learn Reference Topic: Microsoft Foundry observability – Tracing for runtime monitoring vs. Evaluation pipelines for release gates