What actually makes Agentic RAG different from standard...

Standard RAG runs once, deterministically: embed the query, search, take the top-K, generate an answer — and if that single retrieval attempt returns weak or irrelevant chunks, there’s no mechanism to notice or recover. It’s a pipeline, not a decision process.

Agentic RAG puts an actual decision-making loop around retrieval. The agent decides whether retrieval is even necessary for a given question — some questions don’t need it at all. It decides what to search for, and can reformulate the query if the first attempt comes back weak. It evaluates whether the retrieved material genuinely answers the question, rather than assuming top-K similarity equals relevance, and if it doesn’t, it tries a different query, a different index, or a different retrieval strategy entirely. It can also route different sub-questions to different knowledge bases, or combine retrieval with SQL queries, external APIs, or web search inside the same reasoning loop — a comparison question needing both internal financial data and external competitor data is a case standard RAG would fail on outright, because it only ever gets one retrieval attempt against one source.