Developed and delivered the Grok Agent Evaluation Script for task-based search within the browser-use/browser-use repository, focusing on automating the evaluation process for the grok agent. Leveraging Python and asynchronous programming, the solution integrates AI-driven workflows and API development to perform searches tied to specific tasks, reducing manual testing requirements. This feature established a scalable foundation for quality assurance and benchmarking, enabling faster iteration and clearer performance metrics for agent-driven search tasks. The work emphasized robust validation and feedback loops, utilizing web scraping techniques to support comprehensive evaluation. No major bugs were addressed during this period, reflecting a focus on new feature delivery.