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Agentic RAG

RAG architecture where an AI agent decides when to retrieve, what to retrieve, and how to use the retrieved context, instead of retrieving on every query.

What Is Agentic RAG?

Classic RAG fires a vector search on every user query. Agentic RAG lets an LLM agent reason first: route trivial questions to direct generation, run multi-step retrieval for complex ones, and re-query when the first chunks are insufficient. Tools used include LangGraph state machines, function-calling, and self-reflection prompts. Cuts cost on cheap queries and improves accuracy on hard ones.

How Groovy Web Uses This

Our production multi-agent systems use agentic-RAG when retrieval cost or accuracy matters. We layer retrieval decisions into LangGraph nodes so retrievers fire only when the agent plan requires them.

Need Help with This?

Our AI-First engineers build production systems using Agentic RAG technology. Talk to us.

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