Retrieval-augmented generation solved a real problem: it grounded LLM outputs in facts the model was never trained on. But classic RAG has a ceiling. It retrieves once, stuffs the results into a prompt, and hopes the top-k chunks happen to contain the answer. There's no self-correction, no multi-step reasoning, and no way to recover when the first retrieval misses.Agentic RAG removes that ceiling by putting an LLM-driven agent in the loop — deciding what to retrieve, when to retrieve again, whether the retrieved context is actually good enough, and how to combine multiple sources before answering. This article walks through building one from scratch, step by step, with working code you can adapt to your own stack.