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About this paper
Aditi Singh, Abul Ehtesham, Saket Kumar, Tala Talaei Khoei, Athanasios V. Vasilakos
This survey consolidates the rapidly developing field of Agentic Retrieval-Augmented Generation by introducing a principled taxonomy for its diverse architectures and tracing its evolution from traditional RAG, offering a structured understanding and highlighting future research directions.
Key insights
- Agentic RAG transcends traditional RAG limitations by integrating autonomous AI agents capable of dynamic retrieval management, iterative refinement, and adaptive workflow orchestration.
- Core agentic principles, including reflection, planning, tool use, and multi-agent collaboration, enable dynamic adaptation and problem-solving across various structured workflow patterns.
- Architectural choices for Agentic RAG involve balancing trade-offs between simplicity, scalability, latency, and complexity, implying no single optimal solution for all application requirements.
Summary by alphaXiv.
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