Fabirami worked on optimizing the BedrockLLMAgent prompt template in the awslabs/agent-squad repository, focusing on reducing token usage per interaction without altering the agent’s core functionality or instructions. By removing a redundant sentence, Fabirami improved the efficiency and maintainability of the prompt, which contributes to lower large language model costs and better throughput. The work centered on prompt engineering and agent development, leveraging Python to ensure the refactor preserved reliability and business logic. Although no bugs were fixed during this period, the targeted change enhanced the template’s extensibility and stability, reflecting a thoughtful approach to sustainable code improvements.