Worked on optimizing the BedrockLLMAgent prompt template within the awslabs/agent-squad repository, focusing on reducing token usage without compromising core functionality or instructions. The approach involved refactoring the prompt by removing a redundant sentence, which led to lower per-interaction token consumption and improved throughput for large language model operations. Emphasized efficiency, maintainability, and future extensibility throughout the process. Utilized Python as the primary language, applying skills in agent development and prompt engineering to ensure the template remained reliable and easy to update. No major bugs were addressed during this period, as the primary focus was on feature refinement and optimization.