How to Generate Labeled UI Interaction Data at Scale
Building AI that can click, type, and navigate complex web apps is hard. Real UIs change constantly. Scraping them is brittle. Manual labeling is painfully slow. Most teams hit a wall: they need labeled interaction data, but they do not have enough of it. Synthetic data lets you simulate realistic user sessions at scale so your models see the edge cases, error states, and workflows they will actually encounter in production.
The data bottleneck in UI agent training
Most proprietary SaaS applications have thousands of screens, dynamic content, and subtle state dependencies. A model trained on a thin slice of workflows will drift when it encounters a new layout or a rarely used feature. Companies often spend months or years building a small, curated dataset of labeled clicks and scrolls. That dataset might contain only a few hundred representative tasks. That volume is insufficient for robust testing, especially when you need to cover accessibility, localization, and multi-step workflows. Synthetic data fills the gap by generating new interaction traces that mirror real user behavior without touching production systems.
How synthetic interaction data works in practice
Synthetic UI data is not just random clicks. It is defined by task specifications, user personas, and realistic state management. A good pipeline starts with a task graph: a set of steps, conditions, and expected outcomes. For example, you might define a task like "create a new project and invite a collaborator." The system then instantiates a fresh browser or desktop session, picks a random user persona, and executes the steps using a computer use agent. The agent follows the same navigation patterns a real user would, each with probabilities tuned from real telemetry. Every click, keystroke, scroll, and wait time is recorded. Labels are automatically attached: the task ID, the persona, the success or failure condition, and any intermediate states. This process can run continuously, producing millions of labeled trajectories in weeks rather than years.
Key tradeoffs to consider
- ●Realism vs. control: synthetic data gives you full control over edge cases and rare events, but you must calibrate the simulator so it does not drift from real behavior.
- ●Coverage vs. speed: you can rapidly generate thousands of tasks across many applications, but you must validate a representative sample against real user data.
- ●Labeling cost: actions are recorded and labeled automatically, but you still need to design good task definitions and success criteria.
- ●Privacy and compliance: synthetic sessions run in isolated environments, avoiding the risk of exposing real user data or violating privacy policies.
- ●Maintenance: you must update the task graph and the simulator when the application UI changes. Synthetic data does not reduce the need for ongoing maintenance.
The takeaway: synthetic UI interaction data gives you the coverage and speed you need to train and evaluate agents, but the quality depends on a well-designed task framework and a realistic simulator.
How Coasty fits into this workflow
Coasty runs computer use agents on real desktops and browsers. This lets the system observe and learn authentic interaction patterns, then reuse them to generate synthetic datasets. Coasty does not provide a packaged product or a fixed price list. Instead, it offers a custom service where you define the tasks, personas, and domains you care about. The team works with you to design the data pipeline, produce labeled interaction trajectories, and validate them against real behavior. This contact-led approach ensures the synthetic data matches your exact requirements without generic templates.
If you need large volumes of labeled UI interaction data for training or evaluation, synthetic data is the most practical path forward. To explore how Coasty can help you build a custom dataset for your use case, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .