Auditing AI Agents in Snowflake, Part 2: LLM-as-a-Judge Evaluation Pipelines How do you automatically evaluate whether an AI agent’s response is “good”? This guide shows how to build custom …

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A practical guide to building LLM-as-a-judge evaluation pipelines in Snowflake SQL to automatically assess AI agent response quality. Covers four core evaluation metrics — groundedness (hallucination detection), answer relevance, safety/compliance, and comprehensiveness — each implemented as a SQL function wrapping Snowflake's CORTEX.COMPLETE. The pipeline samples recent conversations, runs all judges in parallel, parses JSON scores, computes a weighted composite score, and flags responses as PASS, REVIEW, or CRITICAL. Includes automation via scheduled tasks, email alerting on critical safety issues, domain-specific judge customization (HIPAA, FINRA), and cost estimates (~$2–5 per 100 samples with 4 judges). Part 2 of a 3-part series; Part 3 will build a natural-language Auditor Agent for compliance teams.

Auditing AI Agents in Snowflake, Part 2: LLM-as-a-Judge Evaluation Pipelines

Step 2: Create a Source View for Evaluation