{"id":41108,"date":"2025-10-26T21:29:09","date_gmt":"2025-10-26T20:29:09","guid":{"rendered":"https:\/\/www.dbi-services.com\/blog\/?p=41108"},"modified":"2025-10-26T21:33:46","modified_gmt":"2025-10-26T20:33:46","slug":"rag-series-agentic-rag","status":"publish","type":"post","link":"https:\/\/www.dbi-services.com\/blog\/rag-series-agentic-rag\/","title":{"rendered":"RAG Series \u2013 Agentic RAG"},"content":{"rendered":"\n

Introduction<\/h2>\n\n\n\n

In earlier parts, we moved from Naive RAG<\/strong> (vector search) to Hybrid RAG<\/strong> (dense + sparse) to Adaptive RAG<\/strong> (query classification and dynamic weighting). Each step improved what<\/em> we retrieve. Agentic RAG<\/strong> goes further: the LLM decides if<\/em> and when<\/em> to retrieve at all and can take multiple steps (retrieve \u2192 inspect \u2192 refine \u2192 retrieve) before answering. Retrieval stops being a fixed stage and becomes a tool the model invokes when and how it needs to. This blog post will explain the fundamental principles of agentic RAG from a DBA perspective on which you can build on top of all your business logic and governance rules. <\/p>\n\n\n\n

RAG Type<\/th>Decision Logic<\/th>Flexibility<\/th>Typical Latency<\/th>Best For<\/th><\/tr><\/thead>
Naive<\/td>None<\/td>Fixed<\/td>~0.5 s<\/td>Simple FAQ<\/td><\/tr>
Hybrid<\/td>Static weights<\/td>Moderate<\/td>~0.6 s<\/td>Mixed queries<\/td><\/tr>
Adaptive<\/td>Query classifier<\/td>Dynamic<\/td>~0.7 s<\/td>Varied, predictable query types<\/td><\/tr>
Agentic<\/strong><\/td>LLM agent (tool use)<\/strong><\/td>Fully dynamic<\/strong><\/td>~2.0\u20132.5 s<\/strong><\/td>Complex, exploratory, multi-hop<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
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When One Retrieval Isn\u2019t Enough<\/h2>\n\n\n\n

Example:<\/strong> \u201cCompare PostgreSQL and MySQL indexing approaches.\u201d<\/em><\/p>\n\n\n\n

Traditional (single-pass) RAG<\/strong><\/p>\n\n\n\n