This presentation will explore Agentic RAG, advancing beyond traditional Retrieval-Augmented Generation (RAG) systems to fully realized agent systems. We'll examine how conventional RAG systems, while effective in straightforward query scenarios, struggle with more complex, multifaceted questions that demand deep contextual understanding and dynamic response capabilities. By integrating advanced features such as query planning, contextual memory, and tool usage, Agentic RAG systems can handle sophisticated tasks beyond question-answering, including detailed comparisons and summarizations.

Additionally, we will discuss the development and capabilities of comprehensive agent systems that incorporate multiple layers of agentic reasoning for enhanced performance. These systems utilize multi-turn interactions, reflective learning, and personalization to significantly enhance functionality, enabling them to manage more nuanced and complex tasks.