The SQL Agent Eval Gap: Verifying Live Database Queries Against Ground Truth
JuniorLeg6988 · reddit · 2026-08-13
A developer highlighted an underserved failure mode in evaluating agents that write and execute SQL: the query runs successfully and returns plausible rows, but the data is fundamentally wrong due to incorrect joins, filters, or stale schema understanding.
Because static evaluation sets with "golden" answers fail as underlying data changes, major platforms (LangSmith, Braintrust, Arize) generally rely on reference-free LLM-as-a-judge, lacking out-of-the-box live data verification.
To solve this, the developer is considering building a dedicated tool that connects to the database and independently queries it to verify the agent's output against current ground truth. They are currently surveying the community to gauge the frequency of this issue and whether developers would pay for such a solution.
Related event: SQL Agent Evaluation Pitfalls: Successful Queries with Wrong Data(2 posts)→
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