{
  "id": 10609,
  "url": "https://arxiv.org/abs/2607.13230v1",
  "title": "AI-Native Insurance for Agentic AI: Pricing, Underwriting, and End-to-End Automation",
  "summary": "Agentic AI introduces new insurance challenges because autonomous AI systems can make decisions, invoke tools, modify external environments, and interact with third-party services. This paper develops an AI-native mathematical framework for underwriting, pricing, and contract design for agentic AI deployments. A deployment is represented by a risk state that captures autonomy level, operational authority, permission exposure, governance maturity, and dependency concentration. The framework maps ",
  "authors": "Quanyan Zhu",
  "category": "research",
  "topics": "regulation,jobs-economy,agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-14T19:46:21.000Z",
  "fetched_at": "2026-07-16T05:10:56.605Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/10609",
  "original_url": "https://arxiv.org/abs/2607.13230v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}