AI Coding Agent Evaluation & Data Annotation
Worked on evaluating the performance of a coding agent by testing its ability to execute tasks based on structured instructions provided in markdown files. The project involved reviewing task outputs, verifying correctness, and comparing results against expected human-level performance. Focused on code quality, logical accuracy, and adherence to instructions, while identifying edge cases and inconsistencies in generated outputs. Maintained consistency in evaluation standards and ensured high-quality feedback to improve the agent’s reliability and task completion accuracy.