Agentic RAG is a retrieval system where the LLM agent autonomously decides when, how, and what to retrieve rather than executing fixed retrieval pipelines.
Unlike traditional RAG that retrieves documents first and generates answers, agentic RAG lets the language model:
- Determine whether retrieval is necessary for a query
- Rewrite or decompose queries for better results
- Iterate through multiple retrieval steps
- Evaluate whether retrieved documents are relevant
- Combine multiple information sources before generating final answers
This matters because enterprise AI workloads (supply chain optimization, customer support, legal research) require intelligent reasoning about when and what to retrieve. As of April 2026, agentic RAG has shifted from experimental exploration to core enterprise infrastructure. The vector database becomes the central memory store that the agent queries dynamically, not a static knowledge layer.
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