How to Generate Labeled UI Interaction Data at Scale with Synthetic Data
Most teams building AI agents that use computers hit the same wall: they need lots of labeled UI interaction data, but real data is fragmented, expensive, and full of privacy risks. You can label a few dozen examples by hand, then you hit diminishing returns. Synthetic data lets you generate high-quality, labeled interaction trajectories at the scale your models need.
The real cost of labeled UI interaction data
Building a labeled UI dataset usually means one of two expensive paths. You can scrape hours of real user sessions and pay people to annotate every click, scroll, and field entry. Or you can build internal user flows and pay for controlled testing. In practice, labeling a single realistic UI task can cost between $50 and $200, depending on complexity and domain. For a model that needs thousands of diverse examples, that budget explodes quickly. Even when you have a clean source of raw logs, you still need to map each action to a semantic label, handle edge cases, and maintain consistency across annotators. The time and effort quickly outweigh the value of a small, noisy dataset.
Why synthetic UI data is different
Synthetic UI data is generated by simulating realistic computer use in ways that mirror actual workflows. Instead of relying on rare user sessions, you define the environments, tasks, and expected actions. Agents then explore those environments, making decisions just as a human would. Every interaction is automatically labeled because you define the task structure upfront. This means you can generate thousands of labeled examples in a single run, each with complete context. The key is that the synthetic interactions must behave like real ones: realistic layouts, edge cases, and unexpected user paths. If the generated data looks too perfect, models will struggle on production systems that are messier and more dynamic.
Practical approaches to generate synthetic UI data
Teams typically start with three core techniques.
- ●Task definition and environment setup: you specify the UI layout, controls, and objectives. This can be based on existing wireframes or recorded sessions from your product.
- ●Agent-based simulation: autonomous agents navigate the UI, filling forms, clicking buttons, and responding to system messages. The agents follow rules or policies that reflect realistic user behavior.
- ●Data capture and labeling: every action is logged with full context, including timestamps, element properties, and semantic labels. You get ready-to-use trajectories without any manual labeling.
The biggest win is that you can iterate on synthetic data in days, not weeks. Change a task definition, run the agents again, and you have a fresh dataset. This speed lets you continuously refine your model based on what it actually does in the simulated environment.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers. This gives it a unique edge: the agents experience the same UI behavior, edge cases, and system feedback that your users will. Coasty can capture realistic interaction data and produce synthetic datasets and trajectories tailored to your specific workflows. The service is custom and contact-led, meaning you work directly with the team to define your use case and data requirements. There is no fixed package or public price list, what you get is shaped around your project needs.
If you need labeled UI interaction data at scale without the manual labeling overhead, synthetic data is a practical path. Coasty offers a custom synthetic data service built on real computer use agents. To explore how it can support your project, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .