Question

You operate a generative AI chat agent deployed in Azure AI Foundry. Support engineers report intermittent failures that are hard to reproduce, and you need to enable end-to-end diagnostics so that individual request traces, tool call spans, and exceptions can be queried and correlated with production incidents over the past 30 days. Which action best establishes this observability capability with minimal custom code?

Accepted Answer

Enable tracing on the project by connecting an Application Insights resource so instrumented spans and exceptions are exported and queryable via Kusto (KQL).

Answer

Configure a scheduled automated evaluation job that runs groundedness and relevance metrics against sampled production responses.

Answer

Turn on the content safety filter for the deployment to capture and log all blocked harmful-content requests.

Answer

Write a custom scoring script wrapper that manually writes each request payload to a blob storage container as JSON files.