We examine how hierarchical taxonomies and multi-level schemas have been applied in data labeling pipelines, whether they improved efficiency or reduced resource use, and how this approach relates to modern data-centric AI techniques (like weak supervision or active learning). We also discuss how hierarchical labeling could integrate with emerging architectures such as the MCP(Model Context Protocol) for agent-based AI, and consider potential benefits for AI safety and fairness (through more con
