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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Agentic RAG layer removed, improving retrieval accuracy and speed
An author found that removing an agentic Retrieval-Augmented Generation (RAG) layer from their system led to improved retrieval accuracy. The complex setup, which included self-correcting loops and a grader agent, was r…
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RAG vs. Fine-Tuning: Choosing the Right LLM Strategy Based on Failure Type
This article explores the strategic choices between retrieval-augmented generation (RAG) and fine-tuning for large language models, suggesting a layered approach based on the type of failure encountered. Prompting is pr…
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AI Agents Gain Autonomy with Agentic RAG for Enterprise Knowledge
Enterprise AI agents are evolving beyond simple chatbots, with new architectures like Agentic RAG enabling them to autonomously search and synthesize information from diverse internal systems. This approach moves beyond…
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Agentic RAG vs. Naive RAG: When Retrieval Systems Need to Think
The article discusses the differences between Agentic RAG and Naive RAG, two approaches to retrieval-augmented generation in AI systems. It argues that Agentic RAG, which allows the retrieval system to think for itself,…
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Customer context graph boosts frontier model analysis of feedback
A new research paper introduces a "customer context graph" to enhance the analysis of customer feedback using frontier language models. This graph unifies customer and business information, allowing AI agents to underst…
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Agentic RAG evaluation budget allocation studied
A new research paper explores the optimal allocation of evaluation budgets in agentic retrieval-augmented generation (RAG) systems. The study, which uses HotpotQA and MuSiQue datasets, analyzes how to best distribute re…
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Agentic RAG vs Traditional RAG in .NET: Implementation and Metrics
This article explores the differences between Agentic RAG and Traditional RAG within the .NET framework, detailing scenarios where each approach excels. It delves into the implementation of Semantic Kernel code and disc…
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Building RAG Systems: From Architecture to Hybrid Retrieval and Agentic AI
This cluster details the architecture and implementation of Retrieval-Augmented Generation (RAG) systems, focusing on how to build them from first principles. The articles explain RAG as a method to enhance Large Langua…
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Agentic RAG: AI agents now decide when and how to retrieve information
Agentic Retrieval-Augmented Generation (RAG) transforms retrieval from a fixed step into a decision-making process for AI agents. Unlike traditional RAG, which retrieves once and generates, agentic RAG allows the AI to …
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Agentic RAG Systems for Enterprise Banking Detailed
This article provides a practical guide for banking engineers on implementing Agentic AI and Retrieval-Augmented Generation (RAG) systems. It details how to build self-reasoning AI retrieval systems by incorporating int…
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RAG Framework Explained for Infrastructure Admins
This article explains Retrieval-Augmented Generation (RAG) from an infrastructure administration perspective. It breaks down RAG into three core components: Retriever, Ranker, and LLM, likening them to a tiered help-des…
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New research enhances AI agent memory, reasoning, and grounding
Researchers are developing advanced methods for AI agents to effectively utilize long-term memory and improve their reasoning capabilities. One approach, Query-Conditioned Reuse (QCR), focuses on how agents can adapt pa…
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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…