{
  "id": 7239,
  "url": "https://arxiv.org/abs/2603.14057v1",
  "title": "Demand-Driven Context: A Methodology for Building Enterprise Knowledge Bases Through Agent Failure",
  "summary": "Large language model agents demonstrate expert-level reasoning, yet consistently fail on enterprise-specific tasks due to missing domain knowledge -- terminology, operational procedures, system interdependencies, and institutional decisions that exist largely as tribal knowledge. Current approaches fall into two categories: top-down knowledge engineering, which documents domain knowledge before agents use it, and bottom-up automation, where agents learn from task experience. Both have fundamenta",
  "authors": "Raj Navakoti, Saideep Navakoti",
  "category": "research",
  "topics": "jobs-economy,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-14T17:58:56.000Z",
  "fetched_at": "2026-07-14T16:33:03.575Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7239",
  "original_url": "https://arxiv.org/abs/2603.14057v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}