A five-agent system now automates end-to-end machine learning pipelines from natural-language goals. The architecture uses RAG and a hybrid recommender to construct Directed Acyclic Graphs. It features a self-healing mechanism that interprets execution errors to adaptively refine code. This reduces manual pipeline engineering for practitioners across 150 diverse machine learning tasks.