{
  "id": 11346476,
  "title": "Argo-Bench: Evaluating Data Agents on Enterprise-Scale Workflows",
  "url": "https://urgent.news/2026/10/01/argo-bench-evaluating-data-agents-on-enterprise-scale-workflows",
  "topic": "ai",
  "section": "AI",
  "published": "2026-10-01T17:35:44.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2610.02122v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}