How Computer Use Agents Capture Real Workflow Data for Synthetic Data
Training and evaluating AI agents needs realistic workflow data, yet collecting it is costly and risky. Real user sessions are hard to scale, often violate privacy, and require expensive labeling. Synthetic data solves the problem by modeling the same patterns, but it has to be grounded in how people actually interact with software.
Why workflow data is hard to get
Desktop and browser workflows are messy. They involve multiple windows, copy-paste chains, file moves, and context switches that change with every session. A single workflow might span dozens of steps, each with unique timings and UI states. Capturing enough of these to build a robust dataset is expensive. You face privacy issues, coordination overhead, and the risk of only seeing a narrow slice of real behavior. A model trained on a tiny, unrepresentative sample will struggle with edge cases and new tools.
Computer use agents as data generators
Computer use agents automate task execution on real desktops and browsers. They can log precise actions: clicks, scrolls, keystrokes, voice commands, and even visual inputs like screen reads. Because they run on live systems, every interaction is recorded in full fidelity. This gives you a raw stream of authentic workflow data that reflects the current state of real software. You can replay the stream, annotate it, and turn it into labeled examples for training or testing models. The key advantage is that the agent mimics human behavior, so the resulting dataset looks like real user sessions rather than a simplified simulation.
Key differences from static recordings
- ●Continuous action streams with timestamps and UI states
- ●Support for multimodal inputs (mouse, keyboard, voice, vision)
- ●Ability to adapt to software updates without manual intervention
- ●Scalable replay and replay variations (e.g., different prompts, workflows)
- ●Built-in logging of error handling and fallback behaviors
Real workflow data is messy and context-heavy. Synthetic data grounded in live agent sessions lets you scale training sets while preserving the complexity people actually encounter.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers. This lets teams capture realistic interaction data and build custom synthetic datasets for training and evaluating agents and models. The service is not a self-serve product with fixed packages. It is a custom, contact-led engagement where you work with the team to define the datasets you need. If you want to explore how Coasty can help you generate synthetic data for your specific workflows, the next step is to book a data call.
If you need realistic workflow data at scale and want to see how a custom synthetic data service can support your AI work, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .