"Naive RAG" is officially a legacy architecture in 2026. If your system just does a vector search and feeds it to a prompt, you're falling behind. Enter Agentic RAG.
What makes it "Agentic"?
Traditional RAG is linear: Question -> Search -> Answer. Agentic RAG is cyclical and autonomous.
Key features include:
- Self-Correction: If the retrieved data doesn't answer the question, the agent recognizes the gap and tries a different search query.
- Multi-Step Retrieval: Agents can fetch data from multiple sources (Vector DBs, Web Search, APIs) over several steps to build a comprehensive answer.
- Reflection: Before answering, the agent "reflects" on whether the retrieved information is actually relevant, filtering out noise.
The Tools of the Trade
Frameworks like LangGraph and Creport have made it easier to build these loops. By integrating MCP (Model Context Protocol), agents can now reach into private databases and specific documentation sites with unprecedented ease.