Arun Prakash Asokan

Agentic RAG adds a “brain” to the RAG pipeline – bringing reasoning, tool use, and adaptiveness – which translates to tangible business value in accuracy, flexibility, and user trust

This workshop is a deep dive into Agentic RAG (Retrieval-Augmented Generation) – an emerging approach that combines the power of LLM-based agents with retrieval techniques to build smarter AI applications. Over an 8-hour session (of course including breaks in between), participants will explore how to move beyond “vanilla” RAG pipelines and infuse them with agentic behavior for greater flexibility and intelligence. The workshop is hands-on, using Google Colab notebooks for each module so attendees can practice concepts live. We’ll leverage LangGraph (a LangChain-based framework for agent orchestration) along with the LangChain ecosystem (vector stores, tools, etc.) to design and implement these systems. The theme is highly relevant in today’s AI landscape – many enterprises are already moving from basic RAG to agent-driven systems to power next-generation assistants. In fact, new frameworks like LangGraph have emerged to meet this need, making now the perfect time to master Agentic RAG development. The workshop will also cover practical tips for building enterprise-grade agentic RAG applications

Big Business Benefits of Agentic RAG

Agentic RAG empowers large-scale, enterprise-ready AI systems by combining retrieval-augmented generation with intelligent, decision-making agents. The result? Smarter, more reliable, and adaptable GenAI solutions.

• Higher Accuracy & Trust: Agents reason through queries, validate retrieved information, and reduce hallucinations – essential for business-critical use cases.
  
  
• Dynamic Multi-Source Retrieval: Unlike traditional RAG limited to one vector DB, agents can pull context from multiple sources—internal files, databases, APIs, or the web—dynamically.
  
  
• Autonomous Problem Solving: Agentic systems can break down complex queries, reformulate them, and perform multi-step retrieval and reasoning with minimal human intervention.
  
  
• Scalable & Adaptable: Agent orchestration supports modular workflows that scale across domains, datasets, and tools—making the solution extensible and future-proof.
  
  

💡 Think of Agentic RAG as adding a decision-making brain to your RAG pipeline—boosting precision, flexibility, and business value.

Prerequisite: 

• Basic Python skills 
• Introductory level exposure to LLMs and RAG
• Familiarity with LangChain or similar frameworks 
• Exposure to vector databases and embeddings

*Note: These are tentative details and are subject to change.

In the world of Generative AI, Retrieval-Augmented Generation (RAG) has been a game-changer, but it's time to push the boundaries even further. In this session, we’ll explore the next evolution: Agentic Knowledge Augmented Generation (Agentic KAG). We’ll dive into how to build Knowledge Graphs from unstructured data, use Graph Databases to organize and connect information meaningfully, and design autonomous AI agents using LangGraph to navigate and reason over these graphs.

By moving beyond simple retrieval, Agentic KAG enables LLMs to generate knowledge-rich, contextual, and insightful outputs — overcoming key challenges faced by traditional RAG and agentic RAG systems. Whether you're a developer, architect, or AI enthusiast, this session will give you a hands-on understanding of how to supercharge your LLM applications with agents and graphs.