{
  "id": 13703,
  "url": "https://arxiv.org/abs/2607.22319v1",
  "title": "Towards Trustworthy and Cost-Efficient Data Integration: From Naïve RAG to Agentic RAG",
  "summary": "Large language models (LLMs) and AI agents have demonstrated strong potential for data integration in zero-shot and few-shot settings. However, they continue to face significant accuracy and cost challenges in enterprise environments due to a persistent knowledge gap. This paper envisions trustworthy, scalable, and cost-efficient integration through knowledge-grounded LLMs and agents operating within a retrieval-augmented generation (RAG) workflow. Here, trustworthiness refers to evidence-ground",
  "authors": "Chuangtao Ma, Arijit Khan",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-24T13:58:44.000Z",
  "fetched_at": "2026-07-27T05:10:06.638Z",
  "source_slug": "x-arxiv-cs-ai",
  "source_name": "arXiv cs.AI",
  "source_homepage": "https://arxiv.org/list/cs.AI/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/13703",
  "original_url": "https://arxiv.org/abs/2607.22319v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}