How Computer Use Agents Capture Real Workflow Data for Synthetic Datasets

The problem with real workflow data

Companies that build task automation or decision-support agents often find that real user logs are incomplete. They miss rare workflows, contain noisy inputs, or are blocked by privacy rules. Data labeling for complex tasks, like navigating a multi-step approval process, adds a separate cost and bottleneck. In one large-scale pilot for a document routing system, the team saw that only about 3% of real user sessions covered the full end-to-end workflow they wanted the model to learn. The remaining 97% were partial or irrelevant, making it difficult to train a model that could reliably handle the full task.

How computer use agents create synthetic sessions

Computer use agents are autonomous programs that interact with a computer exactly as a human would: they click, type, scroll, copy, and paste. When these agents run on real browsers and desktop applications, they generate raw interaction logs that are indistinguishable from human behavior. These logs include keystrokes, mouse movements, screen captures, and event sequences. It is the same data you would see in a real user session, just generated by an agent instead of a person. Synthetic workflows can be scripted to target specific business processes, such as onboarding a new employee, submitting a multi-form expense report, or updating CRM records. This targeting means you can fill gaps in your real dataset with high-fidelity scenarios that match your exact use cases.

Quality signals from agent-generated data

Not all synthetic data is equally useful. High-quality synthetic datasets share a few key characteristics: they include the full sequence of actions from start to finish, they avoid repetitive or robotic patterns, and they cover realistic error states and edge cases. Computer use agents can be designed to introduce small variations, such as different order of steps, occasional typos, or navigation detours, to make the data feel more natural. In a test on a web-based form automation task, synthetic sessions generated by an agent matched human performance on downstream evaluation metrics within 4% of human-labeled baselines. That level of alignment suggests that well-designed agents can produce data that is not just voluminous, but also reliable for training and testing.

Tradeoffs to consider

• Agents need to be carefully designed to avoid repetitive patterns and to respect rate limits and security policies.
• Synthetic data must be validated against real user feedback to ensure it reflects legitimate workflows.
• Labeling work is reduced because the agent already produces structured logs, but some edge cases still require human review.

How Coasty fits

Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This experience enables the creation of custom synthetic datasets and trajectories for training and evaluating agents and models. The service is custom and contact-led, so you work with the team to define the workflows, constraints, and quality requirements that match your use case.