Traditional Retrieval-Augmented Generation (RAG) combines external data with generative models, but Agentic RAG takes this a step further by using intelligent agents to refine queries and enhance results. These agents independently manage the retrieval and generation process, improving accuracy and relevance through iterative querying and self-correction. In this talk, we’ll compare standard RAG with Agentic RAG, demonstrating how the agent-driven approach provides greater flexibility, scalability, and performance for complex, real-world tasks. Learn how to implement Agentic RAG to optimize AI-driven systems for more sophisticated and accurate results.