Agentic RAG: Letting the Agent Decide What to Retrieve

Static RAG retrieves once based on the user's query and crosses its fingers. Agentic RAG lets the LLM decide whether to retrieve, what to retrieve, and whether to retrieve again - closing the loop until it has enough information.

Why it matters

For factual single-shot questions static RAG is fine. For exploratory questions, comparisons and multi-source synthesis, agentic RAG dominates. The price is more LLM calls, which the gateway can budget.

How Bhogar AI approaches it

Bhogar AI agentic RAG is built on the planner-executor pattern with a retrieval tool, a query-rewriter tool, and a verifier that decides when to stop. Hard budgets cap calls so quality wins do not become cost disasters.

• Planner-executor agent with retrieval and rewrite tools
• Verifier decides when retrieval is sufficient
• Hard call and cost budgets per query
• Compatible with hybrid and graph retrieval
• Citations cover every retrieved chunk used

What you get

On open-domain QA benchmarks agentic RAG lifts accuracy 12-22 points over static RAG, at 1.5-2.5× token cost.