Agentic RAG vs Classic RAG: From a Pipeline to a Control Loop

Traditional Retrieval-Augmented Generation (RAG) operates as a straightforward pipeline, retrieving documents a single time before generating an answer. In contrast, the more advanced agentic RAG transforms this into a dynamic control loop, enabling the system to reason about evidence, identify gaps, and decide whether to retrieve again or use other tools. This iterative "reason-and-act" capability allows it to tackle complex, multi-part questions that would cause the classic model to fail. While this adaptability is powerful, it introduces significant tradeoffs like variable costs and latency, along with new failure modes such as endless retrieval loops or cascading tool calls.