Paper Reading :Agentic Retrieval-Augmented Generation - A Survey on Agentic RAG

​Details

​We will walk through the paper:

​Agentic Retrieval-Augmented Generation - A Survey on Agentic RAG

​Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG

​Abstract
Large Language Models (LLMs) have revolutionized artificial intelligence (AI) by enabling human-like text generation and natural language understanding. However, their reliance on static training data limits their ability to respond to dynamic, real-time queries, resulting in outdated or inaccurate outputs. Retrieval-Augmented Generation (RAG) has emerged as a solution, enhancing LLMs by integrating real-time data retrieval to provide contextually relevant and up-to-date responses. Despite its promise, traditional RAG systems are constrained by static workflows and lack the adaptability required for multi-step reasoning and complex task management.

​Agentic Retrieval-Augmented Generation (Agentic RAG) transcends these limitations by embedding autonomous AI agents into the RAG pipeline. These agents leverage agentic design patterns—reflection, planning, tool use, and multi-agent collaboration—to dynamically manage retrieval strategies, iteratively refine contextual understanding, and adapt workflows to meet complex task requirements. This integration enables Agentic RAG systems to deliver unparalleled flexibility, scalability, and context-awareness across diverse applications.

​This survey provides a comprehensive exploration of Agentic RAG, beginning with its foundational principles and the evolution of RAG paradigms. It presents a detailed taxonomy of Agentic RAG architectures, highlights key applications in industries such as healthcare, finance, and education, and examines practical implementation strategies. Additionally, it addresses challenges in scaling these systems, ensuring ethical decision-making, and optimizing performance for real-world applications, while providing detailed insights into frameworks and tools for implementing Agentic RAG.

​We are a group of applied AI practitioners and enthusiasts who have formed a collective learning community. Every Wednesday evening at PM PST, we hold our research paper reading seminar covering an AI topic. One member carefully explains the paper, making it more accessible to a broader audience. Then, we follow this reading with a more informal discussion and socializing.

​You are welcome to join this in person or over Zoom. SupportVectors is an AI training lab located in Fremont, CA, close to Tesla and easily accessible by road and BART. We follow the weekly sessions with snacks, soft drinks, and informal discussions.

​If you want to attend by Zoom, the Zoom registration link will be visible once you RSVP. Note that we have had to change and add security to the Zoom link to prevent Zoom bombing.

​
Speaker :
Asif Qamar

​LinkedIn: https://www.linkedin.com/in/asifqamar/

​Technology Leader | AI/Data Scientist | Computer Scientist | Educator | Theoretical Particle Physicist
Technical Leadership

​Primarily interested in technical leadership positions that couple visionary leadership with a high‐octane, technical involvement in
applied AI/Machine learning. What distinguishes me is a technical leadership that brings together extensive, hands‐on technical, AI,
and architectural ability on the one hand and a capacity to bring together a very productive, creative, passionate, and happy team
across geographical boundaries.

​Track record of consistently delivering more than a dozen successful products of enduring value that I was instrumental in envisioning,
crafting the architecture of, doing the early R&D, prototyping, and then building together a dedicated, cohesive, and talented team
around to take the ideas to fruition, through significant projects. Without fail, the products that I have led the creation of are in extensive
deployment and healthy evolution after many years.

​Teaching & Mentoring
Over 20 years of leading, teaching, and mentoring engineers, through team-building around non-trivial projects, classes at universities, workshops, brown bags, and other informal gatherings. Currently, running off-work hours evening workshops in AI/Data-science/Machine-learning and Cloud computing.

​You are welcome to join this in person or over Zoom (https://us02web.zoom.us/meeting/register/tZUvf-uvrTwvHdP9B-vE03j3BapgRypn64CS). SupportVectors is an AI training lab located in Fremont, CA, close to Tesla and easily accessible by road and BART. We follow the weekly sessions with snacks, soft drinks, and informal discussions.