Synthetic Desktop and Browser Trajectories for Agent Training
Training computer-use agents means you need realistic sequences of clicks, keystrokes, and UI states. Real logs are often incomplete, noisy, or tied to proprietary workflows. Using real user sessions at scale risks exposing sensitive data and can trigger compliance issues. Synthetic desktop and browser trajectories let engineers build rich, high-fidelity interaction datasets without these constraints.
Why raw logs fall short
Every organization has a different set of tools: legacy CRMs, SaaS dashboards, custom internal apps. Capturing consistent, high-quality traces across all of them is hard. Logs often miss the context between actions, the error states that follow a failed login attempt, the subtle layout changes that affect button placement, or the uncommon workflows that only a few users trigger. This variability creates blind spots for models that expect clean, uniform sequences.
Making synthetic data realistic
Synthetic trajectories start from realistic UI descriptions. You define the application layout, state machine, and expected flows. Then you run computer-use agents that navigate that environment, encountering expected and edge cases. The key is to anchor the simulation in actual UI assets such as screenshots, DOM structures, and state transitions. This alignment ensures that the agent learns the same affordances a human would see. Studies have shown that models trained on simulated interactions can match or exceed performance on real tasks when the simulation is faithful to the target interface.
Key tradeoffs to watch
- ●Coverage vs. fidelity: You can generate millions of clicks, but if the UI is oversimplified, the model may struggle when it encounters a real product.
- ●State diversity: Synthetic agents should visit varied states, logged out, error pages, dashboard views, to expose the model to edge conditions.
- ●Automation cost: Running agents is compute-intensive. You need to balance batch size, speed, and the number of distinct workflows you want to simulate.
- ●Evaluation alignment: Use the same synthetic data for training and evaluation, or the metrics will not reflect real-world performance.
High-quality synthetic trajectories let you train robust, privacy-safe agents without relying on risky real logs.
How Coasty fits
Coasty runs computer-use agents on real desktops and browsers to capture realistic interaction data. From these sessions, the team can generate custom synthetic datasets and trajectories tailored to your workflows. This is a custom, contact-led service: you define the goals and environments, and Coasty builds the data around them. There is no fixed package or public catalog. The offering is designed to fit the specific needs of your agent or model.
If you need synthetic desktop and browser trajectories for agent training, book a data call with the Coasty team at https://cal.com/coasty/coasty-data-call .