Agentic RAG Turns Retrieval Into a Decision Loop, Not Just a Pipeline Step

Traditional Retrieval-Augmented Generation (RAG) follows a fixed sequence—embed, retrieve, generate—regardless of whether the retrieved content actually answers the query. Agentic RAG replaces this rigid pipeline with a control loop in which an AI agent retrieves information, evaluates its relevance, and retries with a different query or source if the first attempt falls short. Several established patterns implement this idea: Corrective RAG scores chunk relevance before generation and triggers a fallback search if the score is low, while Self-RAG has the model verify that its draft answer is grounded in the retrieved context. Multi-hop and adaptive patterns go further, chaining multiple retrieval rounds where each result shapes the next search query. Graph-based frameworks such as LangGraph are particularly suited to this architecture because conditional edges can route execution backward to retrieval nodes—something a linear chain cannot express.