Why Synthetic Data Is the Real Bottleneck for Computer Use Agents
Computer use agents, systems that control browsers and desktops like humans, look great in demos. But they crash in production. Most teams blame the model, but the real culprit is almost always data. Real-world interaction logs are limited, noisy, and expensive to acquire. Synthetic data is the only scalable way forward, yet many teams still get it wrong.
Real data is a hard ceiling
You cannot scale what you do not have. Enterprise environments have rich task diversity, but capturing it is hard. A 2023 study of enterprise agents found that only 7.2% of user sessions resulted in a successful task completion. The rest involve browser crashes, network errors, or policy violations that corrupt training data. Even when you do get logs, you often lack labels, agents rarely record the exact intent or subgoal. Without labeled, high-fidelity trajectories, you cannot fine-tune or evaluate effectively.
The quality gap
Not all synthetic data is created equal. Randomly generating clicks is useless. Effective synthetic data must mimic real workflows: navigation paths, error recovery, multi-step tasks, and context-dependent decisions. A common pitfall is creating over-simplified scenarios that agents can memorize rather than learn. When synthetic data is too clean, you overfit to the simulation and underperform on the messy real world. The gap between synthetic and real accuracy can be 20-40% in early experiments. Bridging that gap requires realistic environments, not random clicks.
Lifecycle costs
Building synthetic data is not a one-time effort. You need continuous generation to keep up with UI changes, new features, and evolving attack vectors. A well-run synthetic pipeline can produce 10x more labeled trajectories than manual labeling at a fraction of the cost. But the initial setup is expensive. You must curate scenarios, validate generated trajectories against ground truth, and iterate on simulation fidelity. Teams that skip this step end up with synthetic datasets that are quickly stale and unreliable.
The bottleneck is not a lack of data, but a lack of realistic, labeled interaction data that can be generated at scale and kept current.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers, capturing realistic interaction data and trajectories. This enables the creation of custom synthetic datasets tailored to your workflows and edge cases. Coasty’s approach focuses on high-fidelity, labeled interaction data that reflects real user behavior, not oversimplified simulations. Because the service is custom and contact-led, you work with the team to define scenarios, data volume, and quality requirements that match your specific use case.
If you’re stuck with noisy, incomplete real data, synthetic data is the way forward, but only if you build it right. To explore how Coasty can help you generate realistic, labeled interaction data for your agents, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call.