How to Generate Labeled UI Interaction Data at Scale
Building an AI that can use software like a human is hard. You need labeled sequences of mouse clicks, keyboard inputs, scroll gestures, and navigation choices. Real data exists, but it is messy. Manual labeling is slow. Public datasets are small. And some use cases are sensitive enough that you cannot dump live session logs.
The real cost of real UI data
A recent analysis of public browser automation datasets shows an average of 3.2 clicks per task and only 12 unique websites. That is nowhere near the breadth needed for generalist agents. When companies do collect live sessions, they often end up with noise: accidental clicks, incomplete workflows, or privacy-critical steps stripped out. Cleaning that data takes engineering time and reduces signal. You pay for both collection and annotation, and you still hit a ceiling: you cannot easily reproduce the exact same scenario to stress-test your model.
What synthetic UI data actually is
Synthetic UI interaction data is generated from a controlled simulation of a desktop or browser environment. You define a task hierarchy: menus to click, forms to fill, navigation steps, and expected outcomes. The system then produces a trajectory that mimics human-like behavior, including realistic timing, error recovery, and occasional navigational detours. High-quality synthetic data gives you total control over edge cases, privacy constraints, and coverage across many applications. You can generate thousands of distinct scenarios in hours rather than months.
Key tradeoffs and techniques
- ●Control over task diversity: You can target niche workflows (e.g., specific invoicing flows) that rarely appear in public datasets.
- ●Timing realism: Synthetic generators insert jitter and variable keystroke intervals to avoid patterns that models may overfit to.
- ●Noise injection: You can intentionally add accidental clicks, missing selections, or broken paths to train robust error handling.
- ●Privacy scrubbing: You can replace real inputs with safe placeholders before generating interaction sequences.
- ●Replicability: The same seed produces identical trajectories, making it easy to debug and iterate on model performance.
The best synthetic UI datasets are not just simulations. They are carefully designed to match the distribution of real human behavior while giving you control over coverage, noise, and privacy.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction patterns. These agents operate in live environments, so the synthetic trajectories they produce reflect actual UI layouts, application behavior, and edge cases. You can work with the Coasty team to define custom task suites, privacy constraints, and data formats. The service is contact-led and custom, meaning you discuss your specific use case and data needs with their team rather than choosing from a fixed set of plans.
If you need labeled UI interaction data at scale, synthetic techniques give you speed, control, and coverage. To explore how Coasty can help you build a custom synthetic dataset for your agents, book a data call with the Coasty data team.