Retrieval-Augmented Generation (RAG) solved the initial problem of LLM hallucinations by grounding models in factual data. But traditional RAG architectures share a fundamental flaw: they rely on static data. If you are building an AI agent for financial analysis, e-commerce price monitoring, or real-time news aggregation, a vector database updated nightly is useless. Your agents need data from ten seconds ago, not ten hours ago. This requirement has driven the shift from Traditional RAG to Agentic RAG. Instead of querying a stagnant knowledge base, agents are equipped with tools to fetch, parse, and analyze live data from the web autonomously. Architecting a real-time data pipeline for an LLM introduces severe engineering constraints. Your pipeline must be highly reliable, aggressively fast, and capable of returning structured data that fits neatly within context windows. This guide breaks down how to build it.…