Beyond Basic RAG: The Rise of Agentic Retrieval Retrieval-Augmented Generation (RAG) has been the gold standard for grounding LLMs in private data. However, the 'Naïve RAG' pattern—where you blindly fetch the top-k chunks and pass them to an LLM—is hitting a ceiling. The Problem with Naïve RAG Context Bloat: Forcing irrelevant chunks into the prompt costs tokens and confuses the model. Fixed Strategy: A single vector similarity search rarely captures complex, multi-hop reasoning requirements. Hallucination Persistence: When the retrieval fails to find the exact answer, the model often tries to guess instead of admitting it doesn't know. Enter Agentic RAG Agentic RAG transforms the retrieval system from a static pipeline into an autonomous agent. Instead of a hard-coded script, the LLM acts as the orchestrator. It decides: Do I need to search at all? Should I search a vector database, a SQL table, or browse the web? Did I get enough info, or do I need to refine my query?…