Developed foundational language model usage tracking for the IBM/prompt-declaration-language repository, introducing a new entry in the standard library scope to enable the interpreter to monitor and manage AI resource consumption. Leveraging Python and expertise in AI integration and software architecture, the work established a mechanism for usage-based governance and future quota enforcement. By integrating tracking directly with the interpreter scope, the solution supports cost and throughput optimization as well as improved compliance across deployments. This feature lays the groundwork for scalable AI resource management, providing traceability and control over language model operations without addressing bug fixes during the development period.