Agentic RAG reframes retrieval as a reasoning loop—plan, execute, evaluate, repeat—so systems can iterate toward reliable answers instead of relying on one search pass.
Naive RAG’s 3-stage retrieve-read-generate pipeline works for simple, well-structured queries but breaks on multi-step questions, cross-document synthesis and bad initial retrievals.
Four capabilities drive the shift: query routing, query decomposition, self-reflection with retry loops, and tool use such as SQL, calculators, web search and internal APIs.
The trade-off is cost versus capability: naive RAG uses one embedding and one LLM call, while agentic setups add multiple reasoning steps, retrieval iterations and tool calls.
A hybrid model is emerging for production use—send high-volume single-hop queries through naive RAG and reserve agentic workflows for complex or high-stakes tasks.