Why Synthetic Desktop and Browser Trajectories Matter for Agent Training
Training agents that can use desktop software or browse the web is hard. You need thousands of realistic sequences where an agent clicks, types, scrolls, and reads. Real data is often sparse, noisy, or too risky to collect at scale. Synthetic trajectories let you generate exactly the scenarios you need.
Real data is a bottleneck, not a solution
Companies building computer use agents typically scrape forums, record demos, or ask users to share logs. The results are messy. Logs miss metadata like error states or UI changes. Manual labeling is slow and expensive. And you cannot easily simulate rare edge cases or adversarial scenarios without risking production environments. Synthetic data skips the noise and lets you engineer the conditions that matter.
How synthetic trajectories solve the problem
Synthetic trajectories capture full interaction histories in a structured format. You get sequences of UI states, actions, and outcomes that look and behave like real user sessions. You can vary screen layouts, browser versions, error messages, and latency to test robustness. Researchers at major labs have shown that synthetic trajectories improve zero-shot task success by 15 to 30 percent when used to pretrain or fine-tune agents. Synthetic data also cuts labeling costs by 40 to 70 percent compared to manual annotation. The key is creating data that matches the distribution of real interactions while staying under your control.
Key ingredients for high-quality synthetic trajectories
- ●Realistic UI rendering with accurate layout, fonts, and icons
- ●Temporal dynamics such as loading times, animations, and transitions
- ●Stateful environments that remember previous actions and outcomes
- ●Explicit labels for goals, subtasks, and success/failure conditions
- ●Control over edge cases and failure modes that are hard to collect at scale
The best synthetic data is data that looks indistinguishable from real interaction, but gives you full control over every variable you care about.
How Coasty fits into this picture
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This means the synthetic trajectories it generates reflect how humans actually navigate software and the web. Coasty offers a custom synthetic data service: you talk to the Coasty team about your specific use case, and they can produce tailored datasets and trajectories for training and evaluating your agents. There is no self-serve platform and no fixed packages, everything is custom and contact-led.
If you need realistic desktop and browser trajectories for agent training or evaluation, you should book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call to discuss your requirements and explore what is possible.