Synthetic Computers at Scale for Long-Horizon Productivity Simulation

Key points

Collecting long-horizon productivity trajectories from the real world at scale is difficult because of privacy concerns around sensitive user data and the complexity of heterogeneous user environments.
AI agents struggle with realistic long-horizon productivity tasks, such as information gathering, analysis, collaboration, and artifact production, because they lack rich user-specific context and sustained behavior.
Generic, toy-level workflows for agent training do not adequately reflect the context-intensive nature of real productivity tasks, which limits agent effectiveness.
The Synthetic Computers at Scale methodology creates diverse, artifact-rich, user-specific synthetic computer environments by instantiating sampled personas and populating digital workspaces.
Within this environment, it runs long-horizon productivity simulations that mimic about a month of human work, using setup agents that define goals and collaborators and task agents that handle iterative planning and daily execution.
It extracts rich experiential learning signals, both process-level and outcome-level, from these simulations to support agent self-improvement and reinforcement learning.

External research summaries. These are not HDATF publications or measured product results.