Building Agentic RAG, Step by Step: From Static Retrieval to Reasoning Pipelines

The useful part

Retrieval-augmented generation solved a real problem: it grounded LLM outputs in facts the model was never trained on. 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.

How it works

• 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...