AI Observability in Snowflake A Practical Guide to Tracing, Evaluating, and Monitoring AI Workflows in Snowflake Note — This blog has been co-authored with Shubha Muralidhar D As Generative AI …

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AI observability in Snowflake Cortex covers tracing, evaluation, and monitoring of LLM-based workflows. Using TruLens integrated natively into Snowflake, teams can instrument AI agents to capture detailed execution traces and spans, then evaluate outputs using metrics like answer relevance, groundedness, and context relevance via an LLM-as-a-Judge approach. All telemetry is stored in Snowflake tables and queryable via SQL for dashboarding and alerting. A practical walkthrough demonstrates implementing observability on a financial loan agent (LOAN_AGENT_FS) built with Snowflake Intelligence. An upcoming Cortex Agent Evaluation feature (private preview) will add deeper metrics including tool selection accuracy, tool execution accuracy, and logical consistency.

AI Observability in Snowflake

A Practical Guide to Tracing, Evaluating, and Monitoring AI Workflows in Snowflake

Steps to Implement AI Observability in Snowflake Cortex

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Cortex Agent Evaluation (Private Preview)