Developed and delivered the Multi-Agent Energy Optimization System feature for the google/adk-samples repository, focusing on optimizing the power and energy supply chain through real-time data analysis and demand forecasting. The work centered on integrating a Supply Chain AI Agent that leverages Python, BigQuery, and machine learning to process external factors and enhance scenario evaluation speed. By modularizing the AI agent and documenting integration points, the solution improved repository readiness for future domain expansion. The approach emphasized collaborative development and robust quality assurance, resulting in a scalable, reusable workflow that supports faster, data-driven decision-making in energy optimization workloads.