Foundation models (FMs) are at the core of generative AI (genAI), but baseline FMs have a range of limitations in terms of accuracy, relevance, coherence, and domain expertise that require fine-tuning. Retrieval-augmented generation (RAG) provides an integrated approach to optimize the generative output and address these challenges, paving the way to AI agents for intelligent automation. This report, the second in series, traces the evolution of RAG from core engine to comprehensive platform, analyzes the software ecosystems of RAG platforms, and provides examples of vendors in each technology area.