Computer use agents, models that can navigate a desktop or browser, click buttons, fill forms, and reason through tasks, are moving fast. But the real bottleneck isn't model capacity. It's data. Most teams don't have enough high-quality interaction trajectories, and real-world data is risky to touch. Synthetic data is the answer, but only when done carefully.
The data gap is narrower than you think
A 2024 industry study found that teams training agents for browser and desktop tasks often rely on fewer than 1,000 labeled trajectories for production models. That's not enough to capture the edge cases, rate limits, and multi-step workflows that real users encounter. Even if you scrape existing datasets, they usually lack the nuanced reasoning steps and error recovery that make an agent robust.
Real data comes with real costs
Using live sessions introduces risk: account lockouts, privacy violations, and the need for complex consent workflows. A survey of enterprise AI teams reported that 42% of browser automation projects were delayed by data governance hurdles. Synthetic data lets you generate millions of interaction sequences in a controlled environment, eliminating these friction points without sacrificing realism.
Quality beats quantity, but quality is hard
Not all synthetic data is usable. Synthetic trajectories must accurately reflect how users interact with interfaces, handle errors, and switch context. If the synthetic data doesn't match real-world behavior, the agent will fail in production. This is why state-of-the-art approaches now use computer use agents to generate and validate synthetic datasets, rather than relying on rule-based scripts or static templates.
- Avoid templates that generate fake clicks without a realistic task flow.
- Ensure synthetic errors mimic real user mistakes (wrong field, missing input, accidental navigation).
- Validate trajectories with human-in-the-loop review or automated reasoning checks.
Great synthetic data isn't just volume. It's realism plus alignment with the specific workflows and UI patterns your agents will face.
How Coasty fits the picture
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 service is custom and contact-led: you talk to the Coasty data team about your requirements, and they design a solution that matches your needs. There is no fixed package or public pricing, you get what you need for your use case.
If you're building or evaluating computer use agents, synthetic data is no longer optional, it's essential. To see how Coasty can help you generate high-quality, realistic interaction data for your agents, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call.
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