The Agentified OpenCaptchaWorld Benchmark evaluates AI agents on their ability to solve interactive visual CAPTCHA puzzles, a challenging task that requires both visual understanding and precise interaction. The green agent serves 463 CAPTCHA puzzles across 20 types, including counting dice, clicking geometric shapes, rotating objects to match references, solving slide puzzles, matching images, navigating paths, and performing timed interactions. Each puzzle is presented via a web interface, and the agent must analyze the visual content, determine the correct answer, and submit it in the appropriate format (coordinates, indices, sequences, etc.). This benchmark tests capabilities essential for web agents: visual reasoning, spatial understanding, and accurate interaction with dynamic UI elements. We identified several quality issues and flaws in the original OpenCaptchaWorld benchmark, which significantly impacted the performance metrics computation and thus the final evaluation results. Therefore, we systematically validated all 463 puzzles and extended the original benchmark through two major contributions: refined multiple ground-truth label annotations, and extended the original benchmark by introducing two time-based performance metrics which provide additional insight on agents’ efficiency and latency on task completion. Our extensions enable a more comprehensive assessment of agent performance and strengthening the rigor of the benchmark.