{
  "id": 11995,
  "url": "https://arxiv.org/abs/2607.17331v1",
  "title": "Agentic ERP: Multi-Agent Large Language Model Architecture for Autonomous Enterprise Resource Planning",
  "summary": "Enterprise Resource Planning (ERP) systems record transactions reliably but still delegate almost all operational decision-making to human specialists, because classical rule-based automation cannot reason about exceptions and monolithic AI assistants degrade when asked to coordinate across functional boundaries. This paper presents Agentic ERP, an expert-system architecture that combines role-aligned large-language-model (LLM) agents with a risk-tiered human-in-the-loop harness and a graph-base",
  "authors": "Zhihao Liu, Tianyu Wang, Xi Vincent Wang, Lihui Wang",
  "category": "research",
  "topics": "jobs-economy,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-19T16:40:41.000Z",
  "fetched_at": "2026-07-21T05:10:12.656Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/11995",
  "original_url": "https://arxiv.org/abs/2607.17331v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}