Most teams struggle to get enough labeled UI interaction data. Real data is hard to capture: you need to instrument apps, track clicks and keystrokes, and label every action. That process is expensive, slow, and introduces privacy risk. Synthetic data solves this by simulating realistic user journeys and automatically labeling them. You get thousands of trajectories without touching production systems or user devices.
The data problem for computer use agents
Agents that know how to use software need more than static screenshots. They need sequences of mouse movements, clicks, scrolls, and text inputs. A recent benchmark for browser agents showed that only 2 to 4 percent of publicly available datasets contained full session trajectories with correct labels. Most teams have to scrape NLP logs and manually annotate them, which is error-prone and slow. High-quality labeled data is a bottleneck for productionizing computer use systems.
What makes good synthetic UI interaction data
Good synthetic data must look and act like real user behavior. You need to model browser quirks, scroll speeds, subtle hover delays, and occasional mistakes. The generated sessions should hit edge cases and varied page layouts. If the data looks too scripted, models will overfit to artificial patterns and fail in the real world. A common metric is the "human-likeness" score, which measures how closely simulated actions match typical human timing and randomness. Studies show that synthetic datasets with human-likeness above 80 percent reduce model error by 15 to 30 percent compared to purely scripted data.
Techniques to generate realistic synthetic trajectories
- Simulate a desktop or browser environment and replay user workflows at scale.
- Inject noise into mouse movements and timing to mimic human behavior.
- Vary page content, layouts, and dynamic elements to improve generalization.
- Automatically label each event (click, type, scroll) and store the full trajectory.
- Use programmatic test cases to cover workflows that are hard to elicit from real users.
The key takeaway: synthetic data must feel human, not scripted. Add timing noise, varied layouts, and edge cases to match real-world UI complexity.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This approach lets you build custom synthetic datasets that mirror how users actually interact with software. Because the service is custom and contact-led, you work directly with the Coasty team to define your workflows, data volume, and labeled schema. There is no self-serve dashboard or fixed packages. You get datasets tailored to your stack and use case.
If you need labeled UI interaction data at scale, talk to the Coasty data team. Book a data call to discuss your workflows and build a custom synthetic dataset that fits your agents. https://cal.com/coasty/coasty-data-call
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