Synthetic computer environments with long-horizon simulations can generate realistic training data for productivity agents at scale, enabling them to learn from diverse workplace scenarios without human annotation.
Researchers created a system to generate realistic computer environments at scale—complete with folder structures and documents—then simulated AI agents working on month-long productivity tasks within them.