What is agentic RAG and how does it extend basic RAG?

Agentic RAG uses a language model as an autonomous agent that decides when to retrieve, what to query for, and whether retrieved results are sufficient, rather than running a fixed retrieve-then-generate pipeline. The agent can issue multiple targeted queries, use other tools like calculators or APIs, and iterate until it produces a complete answer. This handles complex multi-step questions but increases latency and cost due to multiple language model calls and the risk of loops. It is most valuable for research, analysis, and open-ended tasks.