{
  "id": 113,
  "url": "https://arxiv.org/abs/2607.07858v1",
  "title": "Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting",
  "summary": "Artificial intelligence (AI) is beginning to reshape actuarial practice, particularly in domains that require reasoning over unstructured documents, heterogeneous data sources, and regulated decision workflows. Actuaries now face a design space that ranges from traditional rule-based automation to large language models (LLMs), retrieval-augmented generation (RAG), and multi-agent ``agentic'' systems that plan, retrieve, call tools, and reflect. This paper examines how these emerging architecture",
  "authors": "Robert Richardson, Josh Meyers, Brian Hartman, David Sandberg",
  "category": "research",
  "topics": "regulation,jobs-economy,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-08T18:43:34.000Z",
  "fetched_at": "2026-07-14T14:14:19.967Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/113",
  "original_url": "https://arxiv.org/abs/2607.07858v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}