Developed a robust model output parsing feature for the browser-use/browser-use repository, focusing on enhancing reliability in automated model output handling. The solution introduced a fallback mechanism that extracts JSON data when the primary parser fails, reducing parsing errors and minimizing downstream issues. This approach emphasized resilient error handling and maintainability, with clean, traceable commits supporting ongoing development. Leveraging Python for backend development, the work demonstrated strong asynchronous programming skills and a commitment to stability over rapid bug fixes. The result improved confidence in model outputs, streamlined quality assurance processes, and enabled faster product iterations within the browser-use/browser-use project.