Building agents that can use real interfaces is hard. You need labeled interaction data. Real data is scarce, expensive, and risky. A single mis-click can trigger production alerts or expose sensitive information. Teams often burn months trying to scrape or record enough examples just to validate a model. The bottleneck is not compute. It is reliable, reproducible, and scalable data collection.
The bottleneck is not compute
Most teams underestimate how much data they need. A 2024 study on vision-language models showed that a 10x increase in training samples often improved sample efficiency by only 20 to 30 percent. For UI agents, the curve looks similar. You quickly hit diminishing returns. Yet the time and cost of labeling real interactions stay linear. You might need 100,000 annotated sequences to see meaningful gains. Recording and labeling those manually is a full-time job for months. Synthetic data lets you generate millions of examples on demand, while controlling every click, keystroke, and timing variable.
Why synthetic data works for UI agents
Modern computer use agents need more than screenshots. They need interaction traces: clicks, scrolls, form inputs, navigation paths, and timed events. Synthetic data can model all of this. You define the workflow, the user intent, and the edge cases. The system generates realistic sessions, captures the sequence of events, and attaches labels such as task success, timeout, error, or user intent. Because the system controls every step, you can inject rare scenarios, failed login attempts, unexpected alerts, slow network latency, without risking production. Synthetic datasets are also reproducible. When you iterate on your model, you can regenerate the same distribution of sessions with different random seeds to test robustness.
Key tradeoffs you must manage
- Fidelity vs. control: Higher realism means more realistic edge cases but can be harder to reproduce. A synthetic UI that looks too perfect may miss real-world glitches.
- Coverage vs. cost: You can generate billions of sessions, but if you over-cover rare paths, you waste compute. Prioritize high-value workflows and critical error paths.
- Labeling overhead: Synthetic sessions still need labels. Ensure your pipeline can auto-label based on intent, success conditions, or error detection.
- Legal and compliance: Always validate that synthetic data does not leak real users, secrets, or PII. Use sandboxed environments and anonymize outputs.
The winning strategy is to define your high-value workflows first, then generate synthetic sessions that cover them plus a controlled set of edge cases. This gives you the coverage you need without the cost or risk of real-world testing.
How Coasty fits
Coasty builds computer use agents that run on real desktops and browsers. Those agents experience realistic interfaces, handle dynamic content, and capture authentic interaction traces. Coasty uses this to produce custom synthetic datasets tailored to your workflows and evaluation needs. The offering is custom and contact-led. There is no self-service pricing, no fixed packages, and no public price list. You work directly with the Coasty data team to scope the project, define workflows, and design the synthetic data strategy.
If you need labeled UI interaction data at scale, synthetic data is the practical path forward. To explore how Coasty can build a custom dataset for your agents, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call.
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