This video from IBM Technology explains Agentic Retrieval Augmented Generation (RAG) as an advancement of the standard RAG pipeline. Traditional RAG enhances LLM responses by retrieving relevant data from a vector database and using it as context. Agentic RAG expands on this by employing the LLM as an agent capable of making decisions, such as selecting the most appropriate data source from multiple options based on the user's query. This intelligent routing allows for more accurate and contextually relevant information retrieval, even handling queries that fall outside the scope of available databases. The speaker highlights potential applications in various fields, emphasizing Agentic RAG's ability to create more responsive and adaptable AI systems.