Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG
- Published
- Source
- arXiv
- Paper number
- 024
- Field
- RAG / Agents
- arXiv ID
- 2501.09136
Key points
- Large language models (LLMs) often generate stale or factually incorrect content because they depend on static training data.
- Traditional retrieval-augmented generation (RAG) systems use rigid, predefined workflows, which limits complex multi-step reasoning and iterative response refinement.
- The emerging field of Agentic RAG lacks a unified taxonomy, revealing fragmented system designs and inconsistent terminology.
- This survey establishes a comprehensive taxonomy for Agentic RAG architectures based on agent cardinality, control structure, autonomy, and knowledge representation.
- It systematically analyzes the evolution of the RAG paradigm from Naïve RAG through modular and graph-based approaches, contextualizing the emergence of agentic systems.
- It provides practical guidance for system designers and identifies key open research questions for unifying and advancing Agentic RAG.
Paper links
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