The authors introduce a framework for evaluating and comparing regular, GraphRAG, Modular, and Agentic Retrieval-Augmented Generation (RAG) on semi-structured knowledge bases. They implement nine standardized scenarios spanning simple document retrieval to complex hybrid text-graph integration and agentic multi-step planning. A novel context engineering method is presented to address memory overflow issues in advanced RAG variants through new representations and agentic loop design. This optimization achieves a 19% to 53% reduction in token usage while efficiently managing retrievals. Further analysis reveals a retrieval-generation gap where expanded retrieval does not proportionally improve generation quality. The study suggests that current retrieval-oriented metrics may overstate the benefits of advanced retrieval techniques. These data-driven insights aim to guide the development of production-ready intelligent RAG systems.