How to Generate Labeled UI Interaction Data at Scale
Most teams struggle to get enough labeled UI interaction data for training and evaluating AI agents. Real-world sessions are noisy, fragmented, and hard to repeat. They can also expose sensitive information or expose production workflows to risk. You need high volume and high fidelity, but the obvious path, collecting more clicks, scrolls, and form fills, is slow and expensive. Synthetic data offers a way to generate labeled UI interactions at scale without touching live systems.
The gap between data needs and real-world reality
Modern UIs are complex. A single user flow might involve dozens of DOM elements, async state changes, and nested modals. You need labeled sequences that show not just clicks but also context: which elements are visible, what the user focused on, and how the system responded. Real-world clickstreams rarely include this level of structured annotation. Even when you collect them, you get limited coverage across features, user roles, and edge cases. Studies show that teams with access to 10x more diverse labeled examples often see up to 30% better model calibration and a 20% drop in failure rates on out-of-distribution tasks. That gap comes from data scarcity, not from model capacity.
Why synthetic data helps with UI interactions
Synthetic UI data lets you model the entire system logic in a controlled environment. You can generate full trajectories that include page loads, form submissions, modal toggles, and even error states. The key is to keep the generated interactions grounded in realistic behavior. If you generate random clicks, the resulting sequences don't match how humans actually navigate. A good approach is to simulate user personas with specific goals and constraints, then let an agent or a scripted workflow perform tasks within a browser or desktop environment. This produces sequences that look like real sessions but are reproducible and safe.
Technical patterns for realistic synthetic UI data
- ●Start with a high-fidelity mock or shadow of the target application.
- ●Define personas with clear goals, permissions, and common error paths.
- ●Use agents that can perform tasks like filling forms, navigating menus, and handling modals.
- ●Record full interaction histories: mouse movements, clicks, focus changes, and network requests.
- ●Annotate each step with metadata like element IDs, accessibility roles, and state changes.
- ●Validate that synthetic trajectories pass basic sanity checks: no dead ends, no contradictory actions, and no duplicate steps.
- ●Apply statistical validation: compare distribution of click locations, timing patterns, and error rates to real user data.
The biggest risk in synthetic UI data is over-simplification. Random clicks produce data that does not generalize. Ground your generation in realistic workflows and validate against real user behavior.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. It can generate synthetic datasets and trajectories that reflect actual application behavior, including complex flows and edge cases. The service is custom and contact-led, meaning you work with the Coasty team to define your data needs, scenarios, and quality criteria. There is no self-serve product or fixed price list. You get a tailored solution that matches your stack, your data privacy requirements, and your evaluation goals.
Scaling labeled UI interaction data safely starts with realistic synthetic trajectories and strong validation. If you want to explore how Coasty can build custom synthetic datasets for your agents and models, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .