Computer-Use Agents

Computer-use agents study how AI systems can interpret screens or structured interface data and act through clicks, typing, gestures, and software tools. Common approaches combine vision-language models with action planning, feedback and verification, reusable skills or memory, and training through demonstrations or reinforcement learning in real or synthetic environments. Benchmarks increasingly test long-horizon, cross-application work and measure task outcomes alongside efficiency, reliability, and safety. Agents can handle many routine interactions, but complex workflows remain difficult, and performance depends strongly on the interface, task design, and safeguards against errors or misuse.