How Computer Use Agents Capture Real Workflow Data for Synthetic Data
Training AI models that operate directly on computers and browsers is hard. Real workflows are messy. They involve clicking menus, scrolling, switching tabs, and waiting for pages to load. Capturing this data in the wild is expensive, slow, and often blocked by privacy rules. Teams need a way to generate realistic, high-fidelity interaction data without the risk of exposing real user behavior.
Why workflow data is hard to get
Most teams collect logs from APIs or clickstreams, but those miss the nuance of actual user actions. A real task might involve 20+ discrete steps across multiple windows, context switches, and visual cues. Studies show that 60% of agent evaluation failures stem from a lack of representative interaction data. Without data that mirrors real workflows, models overfit to simplified, scripted tasks and fail in production.
What computer use agents actually do
Computer use agents are AI systems that can control a desktop or browser like a human user. They see screen content, interpret UI elements, and take actions, clicking, typing, scrolling, and even switching tabs. These agents run in controlled environments that mirror real operating systems and browsers. They can repeat workflows thousands of times, capturing every mouse movement, keystroke, and visual change. The result is a trajectory of actions and state transitions that looks and feels like a real user session.
Real tradeoffs and techniques
- ●Replay fidelity: agents can capture frame-by-frame screenshots, mouse coordinates, and UI element IDs. This allows downstream systems to reconstruct exact interactions.
- ●Noise injection: teams can add realistic delays, random scroll offsets, and occasional errors to make datasets harder for models.
- ●Privacy by design: agents run in sandboxed environments, so no personal data or credentials are exposed. This makes synthetic data safe to share internally or externally.
- ●Cross-platform coverage: agents can simulate Windows, macOS, and various browsers, giving teams data across different environments without extra tooling.
- ●Cost per trajectory: with automated replay, the cost per realistic session can drop below $0.10, compared to manual recording which can exceed $5 per hour of work.
The key takeaway: computer use agents let you generate large volumes of realistic, privacy-safe interaction data that mirrors how humans actually use software. This solves the data bottleneck for training and evaluating AI agents.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. The team can produce custom synthetic datasets and trajectories tailored to your workflows. This is a custom, contact-led service, not a self-serve product. If you need realistic, high-fidelity interaction data for training or evaluating AI agents, Coasty can help you build it.
Ready to explore how synthetic workflow data can improve your AI agent training and evaluation? Book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .