The architecture of Agentic RAG is designed to blend retrieval and generation seamlessly. It starts with an input layer for capturing user queries, followed by retrieval components that pull relevant documents using sophisticated algorithms. The system then dynamically selects the most appropriate tools and models, generating responses based on context. A feedback loop ensures continuous improvement by learning from user interactions. This modular approach enables the system to deliver accurate, contextually aware answers while continuously refining its performance, distinguishing Agentic RAG from more static retrieval-augmented generation frameworks.