How Computer Use Agents Capture Real Workflow Data for Synthetic Data
Most teams building AI agents hit the same wall: you need realistic interaction data, but real workflows are messy, sensitive, and expensive to collect at scale. You can’t just scrape screenshots or record users for privacy and compliance reasons. That’s why synthetic data is becoming a practical alternative. The key is capturing authentic behavior in a way that models can learn from.
Why raw logs aren't enough for AI
Traditional logs tell you what actions happened but rarely capture the context, intent, or decision-making process behind them. An API log might show a POST request to /create_invoice, but it doesn’t reveal whether the user was in a rush, how they recovered from a failed step, or how they navigated between tools. For models that need to understand intent and handle multi-step workflows, this context is critical. Synthetic data fills the gap by reconstructing realistic interaction sequences that include motive, uncertainty, and recovery, not just the final state.
How computer use agents actually capture workflow data
- ●Agents run on real desktops and browsers, interacting with the same apps and workflows a human would.
- ●They perform tasks step by step, logging every click, keystroke, and system event.
- ●Agents deliberately introduce variations, different paths, error conditions, and tool combinations, to mirror real-world unpredictability.
- ●Each session is recorded with rich metadata: timestamps, window focus, context menus, and error states.
- ●The result is high-fidelity trajectories that look and behave like human workflows, but are generated at scale without exposing real users.
Real-world impact: concrete numbers
A recent benchmark showed models trained on synthetic trajectories from computer use agents improved task success rates by 12, 18% compared to models trained on raw logs alone. When synthetic workflow data was mixed with a small amount of real-world feedback, success rates climbed further, reaching 23% gains in complex multi-step tasks. These gains come from agents’ ability to generate diverse scenarios, error recovery, navigation variation, and tool chaining, that rarely appear in production logs. Synthetic data lets you explore edge cases and rare workflows without risking production systems or waiting months for user behavior to surface them.
The real value of synthetic workflow data is not just volume. It’s the ability to generate high-fidelity, context-rich trajectories that models can learn from, plus the flexibility to simulate rare scenarios and edge cases at scale.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers, capturing realistic interaction data that mirrors actual workflows. The team can produce custom synthetic datasets tailored to your domain, tools, and evaluation needs. This is a custom, contact-led service, there’s no self-serve portal. The right approach is to talk to the Coasty data team to explore what’s possible for your use case.
If you want high-quality workflow data for training or evaluating AI agents, synthetic data from computer use agents offers a practical path. To see how Coasty can help, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call.