How Computer Use Agents Capture Real Workflow Data for Synthetic Data
Training and evaluating AI agents needs data that shows how humans actually work on computers. Real logs often lack diversity, contain sensitive info, or are too risky to distribute. Synthetic data should look and behave like the real world, but many approaches fall short. One way to get close is to let computer use agents act on real desktops and browsers, then record every action. This captures authentic workflows, click sequences, and error handling without exposing humans to the risk.
Why standard synthetic data misses the mark
Most synthetic data for agents comes from scripted simulations or simple heuristics. These models follow fixed rules, so they never exhibit the messy, context‑aware behavior of real users. An agent that only clicks predefined buttons cannot reproduce unexpected errors, multi‑step troubleshooting, or the way people switch between apps. This gap shows up in benchmarks: models trained on scripted data often fail on real tasks. Studies show that agents trained on realistic interaction logs improve performance by 20 to 35 percent on downstream tasks, while scripted data offers negligible gains.
Agents on real desktops and browsers create authentic traces
When a computer use agent runs on a live system, every gesture, keystroke, and navigation step is recorded. This includes mouse movements, scroll positions, and the order of window switches. Because the agent uses the same UI, tools, and web pages as a human, the resulting trajectory feels natural. For example, an agent completing a form on a real website will encounter the same layout, validation errors, and loading states. The log therefore contains edge cases and system behaviors that scripted generators never see. This approach mirrors how real users compose emails, analyze spreadsheets, or browse e‑commerce sites.
Key steps to turn agent activity into usable synthetic data
- ●Configure the agent with task instructions and access to specific applications and URLs.
- ●Run agents on isolated environments to collect interaction logs without affecting production data.
- ●Clean and label logs by tagging steps, actions, and outcomes for downstream training.
- ●Mix synthetic logs with real examples to balance cost, diversity, and privacy constraints.
The most reliable synthetic data for agents comes from letting agents act on real systems rather than simulating those systems.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This allows teams to generate synthetic datasets that mirror actual workflows and surface edge cases. Coasty’s offering is custom and contact‑led: you discuss your specific needs and receive a tailored data solution. There is no self‑serve platform or fixed package. Reach out to the Coasty data team to explore how synthetic data can improve your agent training and evaluation.
If you need realistic interaction data for AI agents, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call to discuss your requirements.