Agentic Retrieval-Augmented Generation (RAG) enhances traditional RAG systems by giving large language models more control over the retrieval process. Instead of a single retrieval step, agentic RAG involves a planning and refinement loop where the model can decompose queries, iteratively retrieve information, select appropriate tools, and reflect on the evidence. This approach aims to improve the robustness and capability of AI systems for complex, multi-step enterprise questions, though it introduces challenges in latency, cost, and debugging. AI
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IMPACT Agentic RAG offers a more sophisticated approach to enterprise AI, enabling systems to handle complex investigations and improving response accuracy.
RANK_REASON The article describes a novel technique for improving LLM retrieval systems, akin to a research paper or technical deep-dive. [lever_c_demoted from research: ic=1 ai=1.0]