OSWorld Style Synthetic Benchmarks for Computer Use Agents
Training and evaluating AI agents that can control a computer is hard. Real-world interaction data is expensive to label and risky to use. Synthetic data offers a way to produce large volumes of realistic trajectories without those downsides. This post explains how to build OSWorld-style synthetic benchmarks for computer use agents.
What OSWorld-style benchmarks actually measure
OSWorld evaluates agents on tasks like navigating a desktop, opening apps, and completing workflows. The key is that the benchmark must reflect how real users interact with a system. An agent that succeeds only on scripted buttons fails when encountering realistic layouts, error dialogs, or window management. OSWorld-style benchmarks require thousands of diverse trajectories, not just a few handcrafted examples.
Why synthetic data is a practical solution
Producing a benchmark of 10,000 real user sessions costs time and money. Each session needs careful labeling: what the user did, what they saw, and what they wanted to accomplish. Synthetic data flips this model. You can generate millions of trajectories with controlled variation, then label them once. Studies show synthetic data can reach 80-95% annotation quality when the underlying simulation is accurate. That means you get massive coverage and consistent standards without scaling a human labeling team.
Key tradeoffs to consider
- ●Simulation realism: The more the synthetic environment matches real UI elements and behaviors, the higher the benchmark reliability.
- ●Task diversity: Synthetic data must cover complex workflows, not just simple clicks, to expose edge cases.
- ●Labeling consistency: Automated labeling pipelines need guardrails to avoid hallucinating actions or missing states.
- ●Control over difficulty: You can adjust task complexity, success conditions, and failure modes to test harder or easier behaviors.
A strong synthetic benchmark starts with a realistic simulator and ends with high-coverage, human-verified labeling.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers. This lets the team capture realistic interaction data and turn it into synthetic datasets and trajectories. The approach focuses on custom, contact-led projects where teams specify their benchmark needs and evaluation criteria. Coasty does not offer a self-serve product or fixed packages. Instead, teams work with the Coasty data team to design and produce synthetic data that matches their use case.
If you need OSWorld-style synthetic benchmarks for computer use agents, book a data call with the Coasty team to explore what is possible. Visit https://cal.com/coasty/coasty-data-call to schedule a conversation.