GUI Automation

GUI automation studies how AI agents interpret screens or structured interface data, plan actions, and complete tasks in desktop, mobile, and web applications. Common approaches combine vision-language models and visual grounding with planning, action verification, memory and reusable skills, and training from demonstrations, reinforcement learning, or synthetic trajectories. Benchmarks such as OSWorld, AndroidWorld, and WindowsAgentArena test task completion, while newer evaluations probe dynamic interfaces, occlusion, drag interactions, and cross-device workflows. A central challenge is making agents reliable and efficient over long tasks, especially when screenshots, action interfaces, and real-world state changes provide incomplete or misleading feedback.