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Paper · arXiv

Argo-Bench: Evaluating Data Agents on Enterprise-Scale Workflows

Real-world enterprise data science and analytics workflows require reasoning across dozens of tables, performing statistical analyses, and acting on the results. Established text-to-SQL benchmarks evaluate query generation alone, and audits have found their answer keys frequently wrong. Because real enterprise warehouses are too sensitive to release, these benchmarks are built on public datasets where a business event fits in a single table. We introduce Argo-Bench, an evaluation framework comprising 210 data science and analytics tasks. Drawing on public data, peer-reviewed industry literature, and regulatory filings, we simulate a food delivery platform in New York City at true scale, w...

AuthorsGabriel Tomitsuka, Arman Raayatsanati, Emma Xing et al.
Primary categorycs.CL
Published1 October 2026
Last updated at source1 October 2026
cs.CLcs.AIcs.DB
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