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Agentic AI RAG failures rarely start where teams look for them, in the agent's planning logic. They start one layer down, in retrieval, where an agent acting on wrong context doesn't just answer badly, it executes a wrong action. This article breaks down why RAG for agentic AI needs a different approach than single-turn systems, what agentic AI retrieval requires once an agent reasons across multiple steps, and why fixing agentic RAG has to come before adding more orchestration, if AI agents RAG is going to hold up outside a demo.