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
5 day(s) with sentiment data
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RAG's roots traced to early 2000s IR research, not LLMs
A new paper argues that Retrieval-Augmented Generation (RAG), often seen as a novel LLM paradigm, has deep roots in earlier information retrieval and question answering research. The authors trace RAG's core concepts, s…
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Agentic RAG enhances query handling beyond classic RAG limitations
Classic retrieval-augmented generation (RAG) struggles with complex queries involving multi-step reasoning, differing vocabularies, or fragmented evidence. Agentic RAG introduces more complexity but enhances performance…
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AI workshop on Agentic RAG for Network Operations nearly sold out
A workshop focused on Agentic Retrieval-Augmented Generation (RAG) for network operations is nearing capacity. Scheduled for August 25, 2026, the event will cover practical AI applications for network engineers, NOC, an…
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Conversational AI merges sales and education with human-reviewed answers
A new approach to conversational AI integrates education and sales by embedding answers directly into customer interaction flows, eliminating the need for separate FAQ pages or documentation. This system uses a two-memo…
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Intellibooks details 5 RAG architectures for enterprise AI in 2026
Intellibooks has outlined five key Retrieval-Augmented Generation (RAG) architectures that are crucial for enterprise AI applications in 2026. These architectures aim to enhance the accuracy and reliability of AI respon…
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Agentic RAG addresses AI's 'expired facts' problem
Retrieval-augmented generation (RAG) is a common technique to ground AI models with current information, preventing hallucinations by allowing them to look up facts before answering. However, standard RAG implementation…
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AI Concepts for 2026: A Beginner's Guide to Key Terminology
This article serves as a beginner's guide to 17 essential AI concepts that will be relevant in 2026. It highlights how the AI conversation has evolved beyond basic chatbots and LLMs, now incorporating terms like agentic…
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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…
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OpenAI advances AI agents for science and safety; Google DeepMind funds multi-agent research
OpenAI is advancing scientific computing and AI safety through several initiatives. The company has released a new benchmark, GeneBench-Pro, to evaluate AI agents' ability to handle complex biological data. OpenAI is al…