From Naive RAG to Agentic RAG: A Vision for Trustworthy Data Integration

A new arXiv paper (2607.22319) outlines the evolution of retrieval-augmented generation (RAG) for enterprise data integration, moving from classic RAG through GraphRAG and KG-RAG toward Agentic RAG. The authors argue that large language models and AI agents still face accuracy and cost challenges due to a persistent knowledge gap. They propose knowledge-grounded LLMs and agents operating within a RAG workflow to achieve trustworthy, scalable, and cost-efficient integration. Trustworthiness is defined as evidence-grounded, verifiable reasoning that is transparent, robust against hallucination, and consistent. The paper traces the paradigm shifts that bridge parametric and contextual knowledge, culminating in autonomous multi-agent systems that adaptively manage data integration tasks.