OSWorld Style Synthetic Benchmarks for Computer Use Agents
Most computer-use agents still rely on a handful of public benchmarks or small internal test suites. That leaves large blind spots. Real interaction data is hard to obtain at scale, and privacy or security concerns limit what teams can safely collect. Synthetic benchmarks close those gaps by generating diverse, controllable scenarios that mirror the complexity of real desktop environments.
What OSWorld measures and why it matters
OSWorld is a recent benchmark that evaluates agents on real desktop tasks like navigating file systems, editing documents, and using web apps. It reveals how well agents handle multi-step workflows and how they cope with unexpected UI changes. The catch is that OSWorld captures only a slice of the possible interaction space. Adding more diverse tasks requires either exhaustive real-world data collection or a way to generate new scenarios at scale.
Why synthetic benchmarks are a pragmatic solution
- ●You can generate thousands of unique tasks by varying parameters like user intent, UI layout, and error paths.
- ●Synthetic tasks can be designed to probe edge cases that rarely appear in real-world logs.
- ●They let you control privacy and security, since no actual user data is touched.
- ●You can iterate on benchmarks faster than waiting for real-world usage to reveal new scenarios.
The key takeaway: synthetic benchmarks give you statistical power and control that real-world data can't match. They expose weaknesses you would otherwise miss.
How synthetic benchmarks are built
A typical synthetic benchmark for computer use agents follows these steps. First, define a schema of actions: mouse clicks, keyboard inputs, scrolling, and text editing. Next, generate task specifications that describe a goal, constraints, and possible intermediate states. Then create a simulated environment that mirrors a real desktop, making UI elements clickable and observable. Finally, let an agent execute the task, record its trajectory, and score performance. Each iteration can shuffle parameters to expand coverage without touching real user data.
Tradeoffs to watch
- ●Simulators may miss subtle UI behaviors that only emerge in complex applications.
- ●Agents can overfit to synthetic patterns and fail in the wild if the simulation is too idealized.
- ●Benchmark design is as important as generation: poorly scoped tasks won’t reveal real weaknesses.
- ●Synthetic data alone is never enough; it should complement real-world logs and live evaluations.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers. This gives it a unique vantage point on realistic interaction data. It can capture natural trajectories, UI variations, and occasional mistakes that you would otherwise struggle to reproduce. Coasty then transforms that raw data into synthetic datasets tailored to your agent, your stack, and your evaluation needs. The offering is custom and contact-led, meaning you work with the team to define scope, data sources, and quality targets.
If you want to build stronger benchmarks and better agents, synthetic data is a practical lever. To see how Coasty can help you generate custom synthetic datasets aligned with your use case, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .