How Computer Use Agents Capture Real Workflow Data for Synthetic Data

Why workflows matter more than raw text

Models need more than just prompt responses. They need sequences of actions, error states, and context switches. Real-world workflows contain these signals. A support ticket system has a canonical path: login, search, filter, open, reply. That path includes specific clicks, form entries, and timeouts. Capturing these sequences at scale is expensive and slow with humans. Synthetic data can automate this capture, but only if it mirrors the real system.

How agents reproduce workflows

Coasty runs computer use agents on live desktops and browsers. These agents follow task descriptions given by humans. They click, type, scroll, and handle navigation just like a user would. Over time, the agents build a library of trajectories that include screen states, actions, and outcomes. Because they operate on real systems, every interaction reflects current UI, validation rules, and system behavior. This produces synthetic datasets that look and feel like human-generated logs, but at a fraction of the cost.

Quantifying the realism gap

Studies show that synthetic data can reach 80-90 percent of human performance on downstream tasks when the underlying workflow is realistic. However, if the synthetic trajectories miss edge cases or error paths, the gap widens. Coasty's approach closes this gap by running on real systems and using human-written tasks. This ensures that the synthetic data includes common paths, rare errors, and system feedback that pure simulation might miss.

Key tradeoffs to consider

• ●Data diversity: Agents can explore more scenarios than manual testers.
• ●Cost: Synthetic workflows are cheaper than manual labeling once the agent setup is done.
• ●Control: You define the tasks, but the agent executes them live.
• ●Latency: New workflows require agent configuration time.
• ●Accuracy: Agents must follow tasks precisely to avoid drifting into unintended paths.
• ●Privacy: Operating on real systems means you must manage credentials and access controls carefully.

How Coasty fits

Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This allows teams to generate custom synthetic datasets tailored to their workflows. The offering is custom and contact-led: you discuss your use case with the Coasty data team, and they design and execute the synthetic data pipeline that fits your needs.