Is RAG Dead? The Rise of Context Engineering and Semantic Layers for Agentic AI

Retrieval-Augmented Generation (RAG) is evolving from simple text retrieval pipelines into a more sophisticated system known as context engineering. While initial RAG approaches improved Large Language Models by providing external data, they face challenges with scalability, relevance, and noise, which can degrade results. The evolution involves using knowledge graphs (GraphRAG) and semantic layers to provide structured, governed, and explainable context for AI. This shift supports the development of agentic AI systems where retrieval is just one step in a broader reasoning loop. This new paradigm aims to make AI more trustworthy, scalable, and capable of reasoning over complex enterprise data and tools.