HomeResearch LibraryAgentic RAG: Turning RAG Systems into Agents
system architectureChapter 2arXiv · 2024

Agentic RAG: Turning RAG Systems into Agents

Akari Asai (University of Washington), Zeqiu Wu (University of Washington)

Abstract

We present Self-RAG, a framework that trains LLMs to retrieve, generate, and critique their own outputs. Self-RAG adaptively retrieves passages and generates reflective tokens to improve output quality.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

Agentic RAG is the evolution of static RAG into a dynamic, self-correcting knowledge system. For enterprise agents, the ability to adaptively retrieve and critique knowledge is essential for maintaining semantic integrity over long workflows.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Key Contributions

  • →Self-reflective RAG
  • →Adaptive retrieval
  • →Critique token generation

Topics

RAGretrieval augmented generationself-reflectionknowledge grounding