How Computer Use Agents Capture Real Workflow Data for Synthetic Data
Most AI models learn from text or static images. That leaves a blind spot for agents that must manipulate software. You need realistic interaction data to train or evaluate them, but building it is slow and costly. Real users are busy, workflows vary, and cleaning messy logs is a nightmare. Synthetic data solves this by reproducing authentic workflows at scale, and computer use agents are the engine that makes it possible.
The problem with real interaction logs
Real datasets are messy. A single task might generate hundreds of UI events: clicks, keystrokes, scrolls, and pauses. They include misclicks, cancelled actions, and retries. Cleaning that data to form clean trajectories takes hours per task. Then there is the cost of recruitment. To get a few hundred human sessions, you need several weeks of scheduling and compensation. For rare workflows or specialized software, that becomes prohibitive.
Computer use agents as data generators
Computer use agents run on real desktops and browsers. They execute tasks like filling forms, moving windows, and inspecting code. Because they are autonomous, they produce a continuous stream of actions that mirror human behavior. This includes predictable behaviors like tab navigation and surprising ones like random retries or backtracking. A single agent can run hundreds of hours in parallel, generating terabytes of interaction data without manual oversight.
Why agents produce realistic trajectories
Agents are not perfect. They make mistakes. They accidentally click outside their target and correct it. They pause to read context before proceeding. These imperfections are exactly what makes the data realistic. If you only train on perfect, polished trajectories, the model will fail when faced with real-world noise. Synthetic datasets that include errors, retries, and backtracking help models generalize better to production scenarios.
Capturing workflow diversity at scale
Different teams use the same software in different ways. A marketing team might upload assets and publish a campaign in a specific order. A compliance team might run checks and generate reports step by step. Human data collection struggles to capture this diversity. Agents can be configured to follow different paths, explore alternative routes, and vary their pacing. This produces a richer set of workflows that better represent real usage patterns.
Building labeled synthetic datasets from agent runs
Once agents have completed tasks, you can label them. For example, you can mark each trajectory as successful, partially successful, or failed. You can annotate specific steps where the model made a wrong decision. Because agents run on real environments, these labels are grounded in actual software behavior. You can then redistribute the labeled trajectories to train or evaluate other models, closing the feedback loop.
The key insight is that computer use agents provide a scalable, realistic source of interaction data. They expose the noise and variability that real workflows contain, which synthetic datasets need to be useful.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. Teams can work with the Coasty data team to build custom synthetic datasets tailored to their software and use cases. This is a custom, contact-led service, not a fixed product or price list. The process starts with understanding your workflows, designing agent tasks, and then running agents to generate and label data at scale.
If you need realistic interaction data for AI training or evaluation, Coasty can help. Book a data call with the Coasty data team to discuss your requirements and explore how synthetic data can accelerate your projects: https://cal.com/coasty/coasty-data-call