{
  "id": 5513,
  "url": "https://arxiv.org/abs/2604.20158v1",
  "title": "Stateless Decision Memory for Enterprise AI Agents",
  "summary": "Enterprise deployment of long-horizon decision agents in regulated domains (underwriting, claims adjudication, tax examination) is dominated by retrieval-augmented pipelines despite a decade of increasingly sophisticated stateful memory architectures. We argue this reflects a hidden requirement: regulated deployment is load-bearing on four systems properties (deterministic replay, auditable rationale, multi-tenant isolation, statelessness for horizontal scale), and stateful architectures violate",
  "authors": "Vasundra Srinivasan",
  "category": "research",
  "topics": "regulation,agents-autonomy,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-22T03:51:52.000Z",
  "fetched_at": "2026-07-14T16:31:48.874Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5513",
  "original_url": "https://arxiv.org/abs/2604.20158v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}