Why Synthetic Desktop and Browser Trajectories Are Critical for Agent Training
Training and evaluating modern AI agents, models that can use a computer or browser, needs high-quality interaction data. Real-world sessions are messy, expensive to capture, and often unsafe to release. Synthetic desktop and browser trajectories solve these problems. They let you generate realistic interaction logs without the risks or costs of live environments.
The data gap for computer-using agents
Most AI agents need logs of mouse movements, keystrokes, clicks, and text inputs to learn navigation and decision-making. Real logs are rare. Companies collect them as a side effect of internal tools, but they rarely have large, labeled collections of varied workflows. When you do find data, it often contains private information or proprietary workflows. Exporting it risks compliance issues. Building your own dataset means hiring operators to manually perform tasks, which scales poorly.
What a synthetic trajectory looks like
A synthetic trajectory is a sequence of actions that mimics a human operating a desktop or browser. It includes timestamps, screen coordinates, element IDs, and text content. A good synthetic trajectory preserves the structure of a real session: the order of steps, the time between actions, and the context in which choices are made. Modern techniques use computer use agents that run on real desktops and browsers to generate these logs. The agents perform real tasks, so the resulting data reflects real user behavior and UI layouts rather than handcrafted scripts. This makes the data more diverse and harder to distinguish from real sessions.
Concrete benefits and metrics
- ●Speed: synthetic trajectories can be generated in days, not months.
- ●Volume: thousands of unique workflows can be created without extra operator time.
- ●Safety: no proprietary or sensitive data leaves your environment.
- ●Control: you can focus on specific tasks or edge cases that are hard to find in production logs.
- ●Cost: synthetic data reduces reliance on manual labeling and expensive data collection teams.
The key is realism. Synthetic data must capture the variability and ambiguity of real computer use, otherwise the agent will overfit to an artificial pattern.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This enables the creation of custom synthetic datasets and trajectories tailored to your workflows. The offering is a custom, contact-led service. You work directly with the data team to define your requirements, and they generate the synthetic logs that match your needs. There is no self-serve platform or fixed package. The focus is on quality and relevance for your specific use case.
If you need synthetic desktop and browser trajectories for agent training or evaluation, the best next step is to talk to the Coasty data team. Book a data call to explore how Coasty can help you build the right synthetic dataset for your agents. https://cal.com/coasty/coasty-data-call