Agentic RAG explained: Smarter retrieval with AI agents

This post introduces and explains agentic RAG as a next-generation implementation of retrieval-augmented generation. It details how AI agents are embedded into the retrieval pipeline to enable deeper reasoning, smarter routing, and more adaptive workflows, contrasting it with traditional RAG. Key components like router agents, multi-agent systems, and planning/reasoning agents are described, along with essential implementation aspects such as language model grounding, data infrastructure, agent orchestration, tool integration, and observability. The post highlights agentic RAG's benefits for complexity, scalability, and grounded knowledge, positioning it as a key advancement for enterprise AI.