Training and evaluating AI agents demands large volumes of high-quality interaction data. Real-world logs are often sparse, fragmented, or locked behind proprietary systems. Synthetic data offers a scalable alternative, but the quality of that data depends on how faithfully it reproduces genuine workflows. Computer use agents provide a practical way to generate synthetic data that mirrors the complexity and variability of human interaction.
The data gap for AI agents
Agents that operate on browsers and desktops need diverse examples of navigation, clicking, typing, and multi-step processes. Surveys from leading AI labs show that teams often lack sufficient labeled interaction data for domain-specific tasks. Without enough varied examples, models struggle to generalize. Synthetic data can fill this gap, but generic simulations rarely capture the subtle cues, error states, and context switches that occur in real workflows.
Why real-time interaction matters
Synthetic data derived from live computer use agents reflects real timing patterns, window management, and browser state. A 2023 study on browser automation found that timing distributions from live sessions differed significantly from static scripts. Live agents naturally generate delays between actions that resemble human behavior. They also produce edge cases such as unexpected pop-ups, scroll positions, and input validation messages. These details are critical for building robust agents.
Capturing realistic workflows
Computer use agents execute authentic tasks by interacting with actual applications and websites. They navigate menus, fill forms, and handle dynamic content. Because they run on real systems, they encounter the same browser quirks, performance variations, and security checks that human users face. This results in synthetic data that includes realistic error states, retries, and alternative paths. Teams can then use these logs to train and evaluate agents in a controlled yet representative environment.
Synthetic data generated by live computer use agents captures the timing, variability, and edge cases of real workflows, providing a reliable foundation for training and evaluation.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. These agents can produce custom synthetic datasets and trajectories tailored to specific domains and workflows. The offering is a custom, contact-led service designed to align with your data needs. You work directly with the Coasty data team to define requirements, scope, and deliverables.
If you need synthetic data that reflects genuine workflow complexity, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call to discuss your requirements.
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