Towards Trustworthy and Cost-Efficient Data Integration: From Naïve RAG to Agentic RAG

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

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ethics.ai (24 July 2026), “Towards Trustworthy and Cost-Efficient Data Integration: From Naïve RAG to Agentic RAG,” evidence record 13703, https://ethics.ai/record/13703 (originally published by arXiv cs.AI).

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