An earlier post on RAG described it as handing a model the right reference right before it answers. That description was accurate, and it was also describing the simplest possible version of the idea: retrieve once, generate once, done. Agentic RAG is what you get when retrieval stops being a fixed step in a pipeline and becomes something an agent decides to do, evaluates, and redoes if the first attempt was not good enough. It is the same retrieval concept from before, placed inside the same think, act, observe loop covered in the agent posts, with the "act" step specifically being a search.
Where the fixed pipeline actually falls short
Traditional RAG runs the same three steps for every query, regardless of what that query actually needs: embed the question, retrieve the closest matching chunks, hand them to the model to generate an answer. This works well for direct lookup questions, where the answer genuinely lives in one or two retrieved passages. It has no good answer for a question that needs comparing numbers from two different documents, or one where the first retrieval attempt returns irrelevant chunks because the query was phrased ambiguously. A fixed pipeline has no mechanism to notice that its own retrieval was weak, it just generates an answer from whatever came back, confidently, regardless of whether that evidence was actually sufficient.
