{
  "id": 303,
  "url": "https://arxiv.org/abs/2607.02703v1",
  "title": "LLMoxie: Exploring Agentic AI for Scientific Software Development",
  "summary": "In this paper, we describe LLMoxie, an institutional AI platform whose three-tiered architecture supports multi-cloud and on-premise inference, a LiteLLM/MLflow control plane for authentication, budgeting, PII masking, and observability, and an application augmentation layer for AI coding agents. Layered on top, an open-source RSE-Plugins ecosystem encodes accumulated RSE knowledge as a Plugin-Agent-Skill hierarchy spanning scientific Python practice, domain-specific knowledge, a six-phase resea",
  "authors": "Landung Setiawan, Anant Mittal, Cordero Core, Anshul Tambay, Carlos Garcia Jurado Suarez, David A. C. Beck et al.",
  "category": "research",
  "topics": "agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-02T18:46:27.000Z",
  "fetched_at": "2026-07-14T14:14:28.433Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/303",
  "original_url": "https://arxiv.org/abs/2607.02703v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}