Open-AutoGLM is an autonomous agent framework designed to perform complex user workflows on mobile devices. By translating natural language instructions into precise sequences of taps, scrolls, and text inputs, the system enables the automation of mobile application interactions and testing.
The platform distinguishes itself through a combination of vision-language processing and reinforcement learning. It converts graphical user interfaces into structured data, allowing agents to parse screen elements and map natural language commands to coordinate-based actions. To ensure reliability, the system employs heuristic-based error recovery to navigate around interface interruptions such as pop-ups, advertisements, and network delays.
The framework provides a secure, containerized environment for executing these tasks, which isolates agent processes to protect sensitive data and maintain audit trails. Additionally, it functions as a training platform where agents refine their decision-making policies through repeated reinforcement learning cycles within virtualized mobile environments.
