Agentic RAG
Definition
An AI agent decides when and how external information is retrieved.
At RAG, retrieved content is used for a response. An agent-driven process can additionally decide whether to search and check the search result or revise the request.
Simply explained
If necessary, a helper looks in the appropriate folder and searches for a gap again.
Why it matters in practice
Helps with questions that require multiple search steps or different sources of knowledge.
Example from business
Example scenario: An agent searches for a delivery condition, checks the hits and asks for missing information.
Limits and pitfalls
- Additional costs and duration; unauthorized access to documents; incorrect answers despite found sources.
Origin & history
In his Agentic RAG tutorial, LangGraph shows the decision between direct response and document search.
Use in mid-sized companies
- Specify sources of knowledge, access rights and test questions; evaluate hits and answers separately.
Frequently asked questions
Is this always better than simple RAG?
No. Additional decisions must have a measurable benefit.
Sources
- Agentic RAG · LangGraph, Agentic RAG
Source verification: 09.10.2026. The sources document definition and classification; use cases are possible scenarios.
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