Retrieval-Augmented Generation has rapidly evolved, from basic RAG pipelines to sophisticated agentic workflows. Yet, as GenAI systems are applied to real-world problems, a critical limitation becomes clear: flat retrieval and isolated agents struggle to reason over interconnected, contextual, and evolving data. 

This full-day workshop takes participants beyond Agentic RAG into the next frontier of GenAI: Graph-Powered, Knowledge-Driven systems. 

Designed for intermediate GenAI practitioners, the workshop explores how graphs fundamentally transform retrieval, reasoning, and orchestration. Participants will learn how to move from document-centric pipelines to relationship-aware AI systems, where entities, connections, hierarchies, and context become first-class citizens. 

The workshop covers how graphs enhance agentic reasoning, enable multi-hop and contextual retrieval, and unlock deeper intelligence across complex data landscapes. Through architecture patterns, real-world problem scenarios, and hands-on design approaches, participants will learn how to build knowledge graphs, design graph-based retrievers, and implement Agentic Graph RAG architectures that scale beyond traditional approaches. 

By the end of the workshop, participants will be able to design GenAI systems that do not just retrieve information, but reason over knowledge, connect data meaningfully, and solve complex, real-world problems with greater accuracy, explainability, and impact. 

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