Agentic RAG Architectures

What is Agentic RAG Architectures?

Agentic RAG architectures are LLM stacks that combine the benefits of RAG retrieval-based answering with the power of AI agents. The agents can be "read" agents that query information from plugins (e.g., database lookups or internet searches), or "write" agents that actually perform an action (e.g., sending an email, updating a database, etc.). Agentic RAG is a hot new area of research in creating advanced AI architectures.

Agentic RAG: Book Excerpts and Blog Articles

Free online book excerpts with full text chapters online and free PDF downloads, and the Aussie AI blog, including related articles:

• Retrieval Augmented Generation (RAG) Architectures: RAG is a fundamental technique in generative AI that extends the knowledge of an LLM without fine-tuning. Rather than train new knowledge in the LLM's parameters, we instead look up the extra information by searching a database. The LLM receives the user's prompt and the extra information found by the RAG lookup (called the "retriever" component). The LLM then uses its summarization and natural language capabilities to answer the user's question, based on the extra RAG text as input context.... more about Retrieval Augmented Generation (RAG) »
• David Spuler, Michael Sharpe, June 2025, Advanced RAG Architectures, Chapter 14, "RAG Optimization: Accurate and Efficient LLM Applications", https://www.aussieai.com/book/rag-book-14-advanced-rag-architectures
• David Spuler, Michael Sharpe, June 2025, Agentic RAG, Chapter 15, "RAG Optimization: Accurate and Efficient LLM Applications", https://www.aussieai.com/book/rag-book-15-agentic-rag

Research on Agentic RAG Architectures

Research papers include:

• Anita Kirkovska, David Vargas, Jul 11, 2024, Agentic Workflows in 2024: The ultimate guide, https://www.vellum.ai/blog/agentic-workflows-emerging-architectures-and-design-patterns
• Dawei Gao, Zitao Li, Xuchen Pan, Weirui Kuang, Zhijian Ma, Bingchen Qian, Fei Wei, Wenhao Zhang, Yuexiang Xie, Daoyuan Chen, Liuyi Yao, Hongyi Peng, Zeyu Zhang, Lin Zhu, Chen Cheng, Hongzhu Shi, Yaliang Li, Bolin Ding, Jingren Zhou, 20 May 2024 (v2), AgentScope: A Flexible yet Robust Multi-Agent Platform, https://arxiv.org/abs/2402.14034 https://github.com/modelscope/agentscope
• Shubham Sharma. November 12, 2024, How agentic RAG can be a game-changer for data processing and retrieval, https://venturebeat.com/ai/how-agentic-rag-can-be-a-game-changer-for-data-processing-and-retrieval/
• Chidaksh Ravuru, Sagar Srinivas Sakhinana, Venkataramana Runkana, 18 Aug 2024, Agentic Retrieval-Augmented Generation for Time Series Analysis, https://arxiv.org/abs/2408.14484
• Jisoo Jang and Wen-Syan Li. 2024. AU-RAG: Agent-based Universal Retrieval Augmented Generation. In Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (SIGIR-AP 2024). Association for Computing Machinery, New York, NY, USA, 2–11. https://doi.org/10.1145/3673791.3698416 https://dl.acm.org/doi/abs/10.1145/3673791.3698416
• Julia Wiesinger, Patrick Marlow and Vladimir Vuskovic, Sep 2024, Agents, Google Whitepaper, https://www.kaggle.com/whitepaper-agents
• Hui Wu, Xiaoyang Wang, Zhong Fan, 14 Jan 2025, Addressing the sustainable AI trilemma: a case study on LLM agents and RAG, https://arxiv.org/abs/2501.08262
• Aditi Singh, Abul Ehtesham, Saket Kumar, Tala Talaei Khoei, 15 Jan 2025, Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG, https://arxiv.org/abs/2501.09136
• Peter Baile Chen, Yi Zhang, Michael Cafarella, Dan Roth, 30 Jan 2025, Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method, https://arxiv.org/abs/2501.18539
• Zitao Li, Fei Wei, Yuexiang Xie, Dawei Gao, Weirui Kuang, Zhijian Ma, Bingchen Qian, Yaliang Li, Bolin Ding, 13 Feb 2025, KIMAs: A Configurable Knowledge Integrated Multi-Agent System, https://arxiv.org/abs/2502.09596
• Mingyue Cheng, Yucong Luo, Jie Ouyang, Qi Liu, Huijie Liu, Li Li, Shuo Yu, Bohou Zhang, Jiawei Cao, Jie Ma, Daoyu Wang, Enhong Chen, 17 Mar 2025 (v2), A Survey on Knowledge-Oriented Retrieval-Augmented Generation, https://arxiv.org/abs/2503.10677
• Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee, 20 May 2025 (v3), AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges, https://arxiv.org/abs/2505.10468
• Antonio Martinez, Apr 3, 2025, Building an Agentic LLM with RAG Using OpenVINO™, https://medium.com/openvino-toolkit/building-an-agentic-llm-with-rag-using-openvino-4d98bef28205
• Zhejun Zhao, Yuehu Dong, Alley Liu, Lixue Zheng, Pingsheng Liu, Dongdong Shen, Long Xia, Jiashu Zhao, Dawei Yin, 6 Aug 2025, TURA: Tool-Augmented Unified Retrieval Agent for AI Search, https://arxiv.org/abs/2508.04604
• Andrew Brown, Muhammad Roman, Barry Devereux, 8 Aug 2025, A Systematic Literature Review of Retrieval-Augmented Generation: Techniques, Metrics, and Challenges, https://arxiv.org/abs/2508.06401
• Jean Lelong, Adnane Errazine and Annabelle Blangero, 22 Jul 2025, Agentic RAG with Knowledge Graphs for Complex Multi-Hop Reasoning in Real-World Applications, https://arxiv.org/abs/2507.16507
• Qiaoyu Zheng, Yuze Sun, Chaoyi Wu, Weike Zhao, Pengcheng Qiu, Yongguo Yu, Kun Sun, Yanfeng Wang, Ya Zhang and Weidi Xie, 21 Aug 2025, End-to-End Agentic RAG System Training for Traceable Diagnostic Reasoning, https://arxiv.org/abs/2508.15746
• Konstantinos I. Roumeliotis, Ranjan Sapkota, Manoj Karkee, Nikolaos D. Tselikas, 18 Jul 2025, Orchestrator-Agent Trust: A Modular Agentic AI Visual Classification System with Trust-Aware Orchestration and RAG-Based Reasoning, https://arxiv.org/abs/2507.10571
• Aditya Nagori, Ricardo Accorsi Casonatto, Ayush Gautam, Abhinav Manikantha Sai Cheruvu, and Rishikesan Kamaleswaran, 30 Jul 2025, Open-Source Agentic Hybrid RAG Framework for Scientific Literature Review, https://arxiv.org/abs/2508.05660
• Francesco Blefari, Cristian Cosentino, Francesco Aurelio Pironti, Angelo Furfaro, Fabrizio Marozzo, 10 Sep 2025, CyberRAG: An Agentic RAG cyber attack classification and reporting tool, https://arxiv.org/abs/2507.02424
• Jinyeop Song, Song Wang, Julian Shun, Yada Zhu, 1 Oct 2025, Efficient and Transferable Agentic Knowledge Graph RAG via Reinforcement Learning, https://arxiv.org/abs/2509.26383
• Yihan Li, Xiyuan Fu, Ghanshyam Verma, Paul Buitelaar and Mingming Liu, 28 Oct 2025, Mitigating Hallucination in Large Language Models (LLMs): An Application-Oriented Survey on RAG, Reasoning, and Agentic Systems, https://arxiv.org/abs/2510.24476
• Akshay Govind Srinivasan, Ryan Jacob George, Jayden Koshy Joe, Hrushikesh Kant, Harshith M R, Sachin Sundar, Sudharshan Suresh, Rahul Vimalkanth, Vijayavallabh, 19 Sep 2025, Enhancing Financial RAG with Agentic AI and Multi-HyDE: A Novel Approach to Knowledge Retrieval and Hallucination Reduction, https://arxiv.org/abs/2509.16369
• Mohanakrishnan Hariharan, Satish Arvapalli, Seshu Barma, Evangeline Sheela, 12 Oct 2025, Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration, https://arxiv.org/abs/2510.10824
• Divy Yadav, Dec 19, 2025, 9 RAG Architectures Every AI Developer Must Know: A Complete Guide with Examples: Architectures beyond Naive Rag to build reliable production AI Systems, https://pub.towardsai.net/rag-architectures-every-ai-developer-must-know-a-complete-guide-f3524ee68b9c
• David Spuler, Michael Sharpe, June 2025, Advanced RAG Architectures, Chapter 14, "RAG Optimization: Accurate and Efficient LLM Applications", https://www.aussieai.com/book/rag-book-14-advanced-rag-architectures
• David Spuler, Michael Sharpe, June 2025, Agentic RAG, Chapter 15, "RAG Optimization: Accurate and Efficient LLM Applications", https://www.aussieai.com/book/rag-book-15-agentic-rag

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