How Computer Use Agents Capture Real Workflow Data for Synthetic Datasets
Training AI agents often fails because teams lack enough realistic interaction data. Public dashboards and synthetic benchmarks show gaps when the real world is messier. Real workflow data, mouse movements, clicks, scrolling, keystrokes, and error states, is hard to get at scale. It is expensive, risky, or simply not available.
Why workflow data matters for agents
Agents that use computers must navigate interfaces, handle partial information, and recover from mistakes. Public datasets rarely include these edge cases. For example, internal SaaS tools often have unique workflows not captured in generic browser automation datasets. A 2023 evaluation of 12 computer use agents found that only 3 performed above baseline on real-world enterprise tasks, indicating that training data quality still limits performance.
Capturing real interaction at scale
To build realistic synthetic datasets, teams need agents that can run on live systems. These agents perform real tasks, file operations, form submissions, navigation across tabs, and record every interaction. This approach captures timing, error handling, and natural variations that scripted benchmarks miss. One synthetic dataset of this type, used to train a customer service agent, increased task success from 62% to 84% on internal tickets after just 3 weeks of training.
Common tradeoffs and techniques
- ●Public benchmarks are smaller and more uniform, making them easier to use but less representative of real workloads.
- ●Running agents on real systems captures edge cases like intermittent errors and UI changes.
- ●Synthetic data from real sessions allows you to scale quickly without manual labeling.
- ●Privacy regulations limit how much you can reuse real user data; synthetic datasets can be anonymized.
The key is not just collecting interactions, but collecting them in a way that reflects the real world. Real sessions, real workflows, real errors.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data and workflow trajectories. This approach enables the creation of custom synthetic datasets tailored to your environment and use cases. Coasty’s offering is custom and contact-led: you talk to the team to define requirements, and they build the data accordingly. No self‑serve products or fixed packages exist.
If your team needs realistic workflow data for training or evaluating agents, the next step is to book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .