Data / Knowledge library
Agentic RAG
RAG systems where agents search, plan and use memory
Why learn Agentic RAG?
Useful when a simple retrieve-then-answer pipeline is not enough for multi-step, permissioned or changing enterprise context.
Where it came from
Agentic RAG combines retrieval over documents or vectors with agent behaviors such as planning, tool use and memory.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Summarize the official positioning for Agentic RAG in one paragraph.
- Identify the buyer, developer, or user workflow Agentic RAG is built for.
- Compare Agentic RAG with a neighboring catalog topic and name the boundary.
- List two implementation risks or adoption questions for Agentic RAG.
- Design a five-question evaluation that would test whether someone understands Agentic RAG.
Documentation and sources
- MongoDB AI agents and RAG docs (www.mongodb.com)
Catalog research notes
- Research snapshot: September 20, 2026. This entry summarizes the linked public documentation and should be refreshed before making version-specific claims.