OSWorld Style Synthetic Benchmarks for Computer Use Agents
Computer use agents are changing how we interact with software. They open browsers, click buttons, fill forms, and navigate complex workflows. But building reliable agents requires high-quality benchmarks. Real-world benchmarks are limited. They are expensive to create. They can be risky to release. Synthetic benchmarks offer a different path.
Why Synthetic Benchmarks Are Needed
OSWorld, AgentBench, and other benchmarks test agents on a few hundred tasks. That is not enough for serious research or production use. You need thousands of scenarios to surface edge cases and measure true capability. Real-world data has limitations. It is hard to obtain at scale. It often contains sensitive information. It can be biased. Synthetic data solves these problems. You can generate millions of tasks that look and feel like real interactions.
How OSWorld Style Benchmarks Work
OSWorld treats a desktop environment as a testbed. An agent receives a natural language instruction and a screenshot. It must take actions, click, type, scroll, to achieve a goal. The benchmark measures success rate and number of steps. Synthetic benchmarks like this rely on three core ideas. First, a controlled environment that mimics a real OS. Second, a model that generates plausible actions. Third, a way to evaluate outcomes automatically. When you combine these, you can spin up thousands of test environments. Each environment runs a different task. You can re-run tests, tweak parameters, and explore edge cases without touching real users or real data.
The Real Tradeoffs
Synthetic benchmarks are powerful but not perfect. The biggest risk is mismatch. If your agent is trained on real browser interactions, a synthetic environment that looks different can mislead it. Simulators often simplify UI layout. They drop subtle visual cues. They limit the diversity of applications. Another issue is action quality. The model that generates actions must be good. If the generator makes mistakes, the benchmark will reward agents that copy those mistakes. Research shows that synthetic environments can improve performance on their own benchmarks by 15, 30%. But lifts often shrink when you transfer to real systems. The key is to keep the synthetic environment as close to reality as possible. Use real screenshots, real DOM structures, and real click patterns where you can.
The most effective synthetic benchmarks are those that mirror real-world interaction patterns, not just visual matches.
Practical Tips for Building Synthetic Benchmarks
Start with a small set of real tasks. Capture screenshots, actions, and outcomes. Build a simulator that reproduces those tasks. Then expand by randomly combining elements. For example, swap form fields, change button labels, or change navigation paths. Keep track of success rates. When you notice a pattern, add more variants to cover it. Use automatic evaluation whenever possible. Check the final state of the task, not just the steps taken. This reduces false positives from agents that guess their way to a result. Finally, validate randomly selected synthetic tasks against real interactions. This catches simulator drift before it skews your results.
How Coasty Fits
Coasty runs computer use agents on real desktops and browsers. This gives access to authentic interaction data. The team can turn that data into custom synthetic datasets and trajectories tailored to your use case. Coasty’s approach is custom and contact-led. There is no self-serve product and no fixed plans. If you need synthetic benchmarks that reflect your actual software stack, talk to the Coasty data team.
Synthetic benchmarks let you evaluate computer use agents at scale without the cost and risk of real-world data. The key is to build environments that stay true to real interactions. If you want to build or refine OSWorld-style benchmarks for your own agents, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .