Agentic RAG
PulseAugur coverage of Agentic RAG — every cluster mentioning Agentic RAG across labs, papers, and developer communities, ranked by signal.
- 2026-05-18 research_milestone Introduction of Agentic RAG as an improvement over static RAG pipelines.
- 2026-05-17 research_milestone Introduction of Agentic RAG as a solution to common retrieval failures in RAG pipelines. source
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Developer builds agentic RAG system from scratch using Python and minsearch
A developer detailed their experience building an agentic RAG system from scratch as part of the LLM Zoomcamp 2026. The process involved creating a retrieval-augmented generation pipeline using Python and a lightweight…
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Local 7B model study dissects agentic RAG for multi-hop QA
Researchers have conducted an ablation study on agentic retrieval-augmented generation (RAG) systems, specifically focusing on multi-hop question answering with a local 7B parameter model, Qwen2.5-7B-Instruct. The study…
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New research reveals critical latent and silent failure modes in LLM agents
Two new research papers highlight critical failure modes in large language model (LLM) agents. The first, "SIMMER," introduces a benchmark for identifying "latent failures" in LLM planning, revealing that even advanced …
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Open-source agentic RAG platform prioritizes config over code
An open-source platform for agentic RAG in customer support has been developed, emphasizing configuration over code for easier updates. The design prioritizes an intent router to efficiently direct queries, reserving co…
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Google Research enhances Gemini Enterprise with Agentic RAG
Google Research has developed a new agentic RAG framework integrated into the Gemini Enterprise Agent Platform, enhancing its Cross-Corpus Retrieval capabilities. This framework is designed to address the limitations of…
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RAG vs. Fine-Tuning: Choosing the Right AI Approach and Evaluating Performance
The discussion around Retrieval-Augmented Generation (RAG) and fine-tuning for AI applications highlights their distinct use cases and potential for combination. RAG is favored for frequently changing information and pr…
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Advanced RAG techniques empower AI to reason and decide during retrieval
This article delves into advanced Retrieval-Augmented Generation (RAG) techniques, moving beyond basic implementations. It explains how Agentic RAG, CRAG, Self-RAG, and GraphRAG enable AI systems to act more like reason…
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Agentic RAG fixes 40% retrieval failure in LLM pipelines
A new approach called Agentic RAG addresses significant retrieval failures in standard RAG pipelines, which are shown to fail up to 40% of the time in production. Unlike standard RAG, Agentic RAG uses an agent to dynami…
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Agentic RAG improves LLM decision-making in production
The article discusses the limitations of standard Retrieval-Augmented Generation (RAG) in production environments, where it can still produce incorrect answers with high confidence. It introduces Agentic RAG as a soluti…
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Agentic RAG empowers LLMs to retrieve information on demand
Agentic Retrieval-Augmented Generation (RAG) offers a more advanced approach to information retrieval than static RAG, which struggles with complex or time-sensitive queries. Agentic RAG empowers LLMs to decide when and…
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AI agents advance with new RAG, simulation, and compliance tools
Researchers are developing advanced agent frameworks to improve AI reliability and efficiency across various domains. Google introduced an agentic RAG system that enhances enterprise query handling by iteratively search…