Most teams focus on model architecture or agents that follow instructions. The real problem often lies in the data: there isn't enough realistic, labeled interaction data to train or evaluate them properly. Synthetic data is the answer, but it's a tightrope walk between realism and control.
Real data is hard to obtain at scale
Computer use agents need thousands of realistic trajectories: mouse movements, clicks, scroll depths, form inputs, and error states. Real-world desktop and browser data is messy and expensive to collect. Privacy rules and security policies often block access to internal tools. Even when you can capture sessions, labeling them is slow and error-prone.
Synthetic data gives you control, but you have to design it well
Synthetic data solves the scaling problem. You can generate millions of sessions that match your exact UI, workflows, and edge cases. But quality varies wildly. Some synthetic datasets look too scripted, causing models to overfit. Others miss critical variations like disabled buttons, permission prompts, or network glitches. The difference between a useful dataset and a noisy one comes down to how the synthetic environment is built.
A realistic baseline can cut training time by 30-50%
Teams that start with a strong synthetic baseline see faster convergence and better out-of-distribution performance. One engineering team reported that adding a synthetic rollout to their internal workflow reduced their trial-to-success rate by 40% and cut human review time by half. Synthetic data is not a shortcut; it's a strategic investment in data quality and speed.
Key tradeoffs to watch
- Control vs realism: You can perfectly control synthetic environments, but if the UI or user behavior doesn't match reality, models won't transfer.
- Coverage: Synthetic data can cover rare edge cases, but you need to explicitly design those scenarios.
- Complexity of state: Generating correct background states, permissions, and system messages is harder than it looks.
The bottleneck is not a lack of models or ideas. It's a lack of high-quality, realistic interaction data that you can generate at scale and control for your specific workflows.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers. This allows it to capture realistic interaction data and produce synthetic datasets and trajectories that mirror actual user behavior. Coasty offers a custom synthetic data service that is built around your specific use cases. There is no self-serve platform and no fixed packages. To explore how Coasty can help you build high-quality labeled datasets for training and evaluating your agents, you need to talk to the data team.
The next step is to book a data call with the Coasty data team. Visit https://cal.com/coasty/coasty-data-call to schedule a conversation about your synthetic data needs.
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