How does RAG differ from an agentic approach, and can they combine?

Classic RAG (retrieval-augmented generation) is a fixed pipeline: retrieve relevant documents for a query, stuff them into the prompt, and generate an answer, one retrieval, one generation. It grounds answers in your data and reduces hallucination, but the flow is static and single-shot. An agent is dynamic: it decides whether, when, and what to retrieve, can issue multiple queries, refine them based on results, and combine retrieval with other tools. Agentic RAG treats retrieval as one tool the agent can call iteratively, enabling multi-hop questions (retrieve, reason, retrieve again). Trade-off: agentic RAG is more capable on complex queries but costs more tokens, latency, and complexity, and can loop. Use plain RAG for straightforward lookups where one retrieval suffices; use agentic RAG when questions require decomposition, multiple sources, or iterative refinement.