Use Cases

Desktop Automation

Operators can automate repetitive office and desktop tasks by controlling GUIs. For example, an Operator might open an email client, copy data from a spreadsheet, and send formatted emails – all by interacting with application windows just as a human would. This is essentially desktop automation (a form of Robotic Desktop Automation, RDA). Such automation uses “software robots” to emulate human actions on applications. By running on any desktop environment, the platform can automate heterogeneous enterprise workflows (e.g. reading from a legacy app on Windows, then updating a web app on Linux). This saves time and reduces errors on tedious tasks, enabling knowledge workers to focus on higher-value work.

Gaming Agents

The platform can host agents that play or test video games autonomously. These AI gaming agents receive the game’s visual frames and produce controller inputs or mouse clicks just like a human player. They can be used for QA testing (spotting bugs or balance issues) or even as advanced NPC opponents. For instance, an AI testing agent can systematically explore game menus, levels, and mechanics, adapting its strategy as it learns gameplay. In practice, AI testing agents act like digital testers: they “examine your application, spot functional issues, and adapt scenarios on the fly” much like a human tester would. This can greatly accelerate game development and allow continuous, rigorous testing without manual effort.

Robotics Automation

Beyond software, Operators can control physical robots. Here, the machine layer connects to a robot’s hardware (sensors and actuators), and the Agent logic sends commands (e.g. move arm, navigate). For example, an Operator might receive camera images from a robot and decide how to move objects on a conveyor belt or navigate a drone through an environment. AI-powered robots are already improving industrial tasks: studies report that robots guided by AI can perform inspections, maintenance, and material handling with higher precision and safety. In warehouses or factories, such Operators could plan paths, recognize objects, and adjust in real time. By abstracting robotic control into a standard platform interface, our Operators bridge high-level AI reasoning with real-world actions.