Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
AI Summary
An arXiv survey (submitted Jan 2025, revised through v4 in 2026) arguing that traditional RAG's static retrieve-then-generate workflow can't handle multi-step reasoning, and proposing Agentic RAG — RAG pipelines augmented with autonomous agents doing reflection, planning, tool use, and multi-agent collaboration. The paper introduces a taxonomy of Agentic RAG architectures by agent cardinality, control structure, autonomy, and knowledge representation, compares design trade-offs across existing frameworks, surveys applications in healthcare, finance, education, and enterprise document processing, and lists open challenges in evaluation, coordination, memory management, efficiency, and governance.
Original excerpt
arXiv:2501.09136 [cs.AI] — Submitted 15 Jan 2025 (v1), last revised 1 Apr 2026 (v4)
Authors: Aditi Singh, Abul Ehtesham, Saket Kumar, Tala Talaei Khoei, Athanasios V. Vasilakos
Large Language Models (LLMs) have advanced artificial intelligence 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their reliance on static training data limits their ability to respond to dynamic, real-time queries, resulting in outdated or inaccurate outputs.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…
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An arXiv survey (submitted Jan 2025, revised through v4 in 2026) arguing that traditional RAG's static retrieve-then-generate workflow can't handle multi-step reasoning, and proposing Agentic RAG — RAG pipelines augmented with autonomous agents doing reflection, planning, tool use, and multi-agent c…
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"Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG" was written by Aditi Singh, Abul Ehtesham, Saket Kumar, Tala Talaei Khoei, Athanasios V. Vasilakos. It is curated in the AI Deep Research vault on Burn 451, which covers ai research agents & methodology.
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Content attributed to the original author (Aditi Singh, Abul Ehtesham, Saket Kumar, Tala Talaei Khoei, Athanasios V. Vasilakos). Burn 451 curates publicly available writing as a reading index. For removal requests, contact @hawking520.