{
  "id": 265,
  "url": "https://arxiv.org/abs/2607.03516v1",
  "title": "AGL-1: The Enterprise AI Governance Layer as a Control Plane for Trusted Enterprise Intelligence",
  "summary": "Enterprise artificial intelligence is moving from isolated experimentation toward operational dependency across copilots, retrieval-augmented generation systems, autonomous agents, and AI-enabled business workflows. As this transition accelerates, the primary enterprise challenge is no longer only model access or inference scale. It is governed intelligence operations: the ability to enforce authorization, preserve contextual lineage, control persistent memory, detect stale or conflicting knowle",
  "authors": "Roopam W. Sure",
  "category": "research",
  "topics": "regulation,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-03T17:42:08.000Z",
  "fetched_at": "2026-07-14T14:14:24.248Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/265",
  "original_url": "https://arxiv.org/abs/2607.03516v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}