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How to Build a Golden-Question Benchmark for Your Data Agents
Data agents require rigorous regression testing using frozen data snapshots and execution-based scoring rather than simple demo-style evaluations.
What: Author Alex Merced outlines a strategy for 'golden-question' suites that use Apache Iceberg snapshots to ensure data stability and gate releases based on objective pass rates.
Why it matters: Data agents are fragile systems whose accuracy is impacted by prompt changes, schema updates, and underlying metric drifts; treating them as standard software deployments is necessary for production reliability.
Takeaway: Assign stable identifiers and owners to every evaluation question and gate CI/CD merges using a threshold of at least 85% pass rate.
Deep dive
- Golden Questions: A set of questions with fixed IDs, owners, expected answers, and metric dependencies.
- Snapshotting: Use Iceberg
CREATE TAGto pin data versions so evaluations remain deterministic over time. - Execution Scoring: Compare results using columnar formats like Apache Arrow rather than comparing query text.
- Gatekeeping: Define thresholds for broad regressions, category-specific failures, and safety boundary violations.
- Data Sources: Source questions from dashboard logs, known failure patterns (e.g., fan-out joins), and real user feedback.
Decoder
- Golden Questions: A curated set of inputs used for regression testing where both the question and the ground-truth result are known.
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