Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG

TL;DR

This survey tackles the limitations of static LLMs by surveying Retrieval-Augmented Generation (RAG) and its evolution toward Agentic RAG, where autonomous AI agents dynamically manage retrieval, reasoning, and workflows. It maps foundational RAG paradigms (Naïve, Advanced, Modular, Graph) and details the agentic paradigm, including core agentic principles, workflow patterns, and a comprehensive taxonomy of architectures (single-, multi-, hierarchical, corrective, adaptive, graph-based, and document-centric ADW). The paper highlights applications across healthcare, finance, education, and legal domains, and discusses practical considerations, benchmarks, and tools to implement Agentic RAG at scale. By articulating comparative analyses and concrete workflows, the work lays a foundation for deploying adaptive, real-time, context-aware AI systems with broad real-world impact.

Abstract

Large Language Models (LLMs) have revolutionized artificial intelligence (AI) by enabling human like text generation and natural language understanding. However, their reliance on static training data limits their ability to respond to dynamic, real time queries, resulting in outdated or inaccurate outputs. Retrieval Augmented Generation (RAG) has emerged as a solution, enhancing LLMs by integrating real time data retrieval to provide contextually relevant and up-to-date responses. Despite its promise, traditional RAG systems are constrained by static workflows and lack the adaptability required for multistep reasoning and complex task management. Agentic Retrieval-Augmented Generation (Agentic RAG) transcends these limitations by embedding autonomous AI agents into the RAG pipeline. These agents leverage agentic design patterns reflection, planning, tool use, and multiagent collaboration to dynamically manage retrieval strategies, iteratively refine contextual understanding, and adapt workflows to meet complex task requirements. This integration enables Agentic RAG systems to deliver unparalleled flexibility, scalability, and context awareness across diverse applications. This survey provides a comprehensive exploration of Agentic RAG, beginning with its foundational principles and the evolution of RAG paradigms. It presents a detailed taxonomy of Agentic RAG architectures, highlights key applications in industries such as healthcare, finance, and education, and examines practical implementation strategies. Additionally, it addresses challenges in scaling these systems, ensuring ethical decision making, and optimizing performance for real-world applications, while providing detailed insights into frameworks and tools for implementing Agentic RAG.

Figures (23)

• Figure 1: An Overview of Agentic RAG
• Figure 2: Core Components of RAG
• Figure 3: An Overview of Naive RAG.
• Figure 4: Overview of Advanced RAG
• Figure 5: Overview of Modular RAG