{
  "id": 6504,
  "url": "https://arxiv.org/abs/2604.00555v5",
  "title": "Ontology-Constrained Neural Reasoning in Enterprise Agentic Systems: A Neurosymbolic Architecture for Domain-Grounded AI Agents",
  "summary": "Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory compliance at the reasoning level. We present a neurosymbolic architecture implemented within the Foundation AgenticOS (FAOS) platform that addresses these limitations through ontology-constrained neural reasoning. We introduce a three-layer ontological framework--Role, Domain, and Interaction ontologies--grounding LLM-based enterprise agents. We formalize as",
  "authors": "Thanh Luong Tuan, Abhijit Sanyal",
  "category": "research",
  "topics": "regulation,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-01T06:59:15.000Z",
  "fetched_at": "2026-07-14T16:32:33.100Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6504",
  "original_url": "https://arxiv.org/abs/2604.00555v5",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}