Synthetic Desktop and Browser Trajectories for Agent Training
Building agents that can handle real desktops and browsers is exciting. The problem is the data. Real interaction logs are rare, fragmented, and often contain sensitive information. They are expensive to collect and hard to share. That creates a bottleneck for anyone trying to train or evaluate computer-use AI. Synthetic trajectories offer a way out.
Why desktop and browser trajectories matter
Agents need to know how to click, type, scroll, and move windows. They need to understand layouts, menus, and error messages. Real logfiles capture some of that, but they rarely cover the full decision process. Most datasets focus on a single task, like a data entry form, without showing how the agent finds, fills, and verifies it. Without a variety of scenarios, an agent will struggle in production.
Real tradeoffs you should know
- ●Real data is accurate but limited in scope and scale.
- ●Synthetic data is highly scalable but needs careful design to match the real world.
- ●Mixing both approaches often gives the best results for training and evaluation.
- ●Quality depends on how faithfully the synthetic environment mimics real UI, not just the actions.
- ●Labeled trajectories (state, action, reward) are critical for reinforcement learning.
Concrete numbers from the field
Studies on synthetic data for robotics and automation show consistent gains. One benchmark found that agents trained on 1 million synthetic trajectories reached a 22 percent higher success rate on real-world tasks compared to agents trained on only 50,000 real trajectories. Another analysis reported a 3x reduction in labeling costs when synthetic data covered 80 percent of the edge cases that were previously missing. These numbers highlight that scale matters, and quality matters even more.
Synthetic trajectories let you explore edge cases, rare flows, and failure modes that would be impractical to collect in the real world. The key is to design the synthetic environment so that the behavior it generates is realistic and aligned with your user journey.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers. By observing how agents interact with actual software, Coasty captures realistic trajectories that reflect real workflows, error states, and user behaviors. This data can be used to build custom synthetic datasets tailored to your applications. The service is custom and contact-led, meaning you work directly with the team to define what you need.
If you want to train or evaluate agents that can handle desktop and browser tasks, synthetic trajectories can help you ship faster and go further. To explore what Coasty can build for your use case, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call.