➡️ What are the well-known problems of naive RAG that Agentic RAG solves and how?
- It performs one retrieval and one generation. If the context is irrelevant, it cannot perform a second search.
- It handles all requests the same way. A simple lookup and a complex multi-hop task go through the same retrieve-then-generate pipeline.
- There is no validation. The system blindly trusts what the retriever returns.
Agentic RAG solves this by implementing decision loops at each stage.
Steps 1-2) A query rewriting agent reformulates the original query. This is not just about correcting typos - it optimizes the query for retrieval: clarifying vague formulations, decomposing complex queries into sub-queries, and expanding abbreviations.
Steps 3-5) A routing agent decides whether external context is needed at all. If not, the retrieval is skipped. If yes, a source selector chooses the best backend for the specific type of query.
Steps 6-7) The source selector directs the query to the most suitable source: vector DB for semantic search, web search for up-to-date information, or structured APIs for tabular data. The obtained context and the reformulated query are combined into a prompt.
Steps 8-9) The LLM generates the initial response.
Steps 10-12) A validation agent (also known as Corrective RAG) checks whether the response is relevant, grounded, and complete. If everything is ok - the response is returned. If not - the system returns to step 1 with a reformulated query.
This cycle is repeated several iterations until a satisfactory response is obtained or the system acknowledges that it cannot answer.
Why this works? each agent acts as a quality gate.
- The rewriter improves the accuracy of retrieval
- The router ensures the selection of the correct source
- The validator checks that the response is grounded
Errors at individual stages are caught and corrected, rather than silently being passed on through the pipeline.
Scheme in above media is just one of possible blueprints of Agentic RAG system. In production, Corrective RAG, Adaptive RAG, Self-RAG, and hybrid search (vector + lexical with reranking) are increasingly being combined depending on latency budget and accuracy requirements.
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