During April 2025, George contributed to the Metta-AI/metta repository by developing a robust training and evaluation pipeline for navigation tasks. He built and integrated a configurable Labyrinth World environment, allowing for flexible map parameters, agent counts, and object properties within the training workflow. Using Shell scripting and YAML for configuration management, George expanded the set of navigation evaluation policies and refactored run configurations to support broader policy testing. He also enabled evaluation against a dedicated database by updating configuration files. His work focused on feature delivery, reproducibility, and end-to-end integration, demonstrating depth in CI/CD and automated testing practices.