Synthetic Data vs Real Data for Training AI Agents
Training AI agents that can actually use computers is hard. Real interaction data is expensive to label, risky to store, and often unavailable. Synthetic data solves these problems. It lets teams train and evaluate agents on vast, privacy-safe datasets that look and behave like the real world.
The real bottleneck: not data quantity, but data quality
Most teams hit the same wall: they need thousands of high-quality trajectories for computer use agents, but real-world data is scarce. A recent comparison of agent benchmarks showed that models trained on synthetic trajectories achieved 23 percent higher accuracy on unseen tasks compared to models trained only on real-world examples. The gap is even larger for rare or security-sensitive workflows. Synthetic data fills the gap by generating millions of realistic interactions without capturing any personally identifiable information. This makes it possible to train on edge cases, adversarial attacks, and complex workflows that would never appear in production logs alone.
Cost and speed: synthetic data wins on both fronts
Labeling real interaction data requires human experts, even for simple tasks. Companies report labeling costs of $5 to $15 per hour for basic browser automation tasks. Synthetic data generation, especially when powered by agents that already understand the UI, cuts this cost by up to 80 percent. Teams can spin up a synthetic environment, generate a month's worth of realistic trajectories in a few days, and iterate quickly. This speed matters for agent development cycles, which can span weeks or months. Synthetic datasets can be refreshed on demand, letting teams keep models aligned with the latest UI changes without waiting for new real-world logs.
Privacy and compliance: synthetic data is inherently safe
Real interaction logs contain passwords, API keys, and personal data. Storing, sharing, or reusing this data can violate privacy regulations and internal security policies. Synthetic data removes these risks entirely. Because it is generated procedurally, it contains no real user credentials or sensitive information. This makes it safe to use in public benchmarks, share with partners, or deploy in regulated industries like healthcare and finance. Teams can train on realistic workflows without ever storing or exposing real user data. This compliance advantage is a major reason many enterprises are shifting from real-only datasets to mixed synthetic-real training sets.
Performance tradeoffs you need to know
Synthetic data isn't magic. Models trained purely on synthetic trajectories sometimes struggle with domain shifts, especially when the real environment is idiosyncratic or poorly documented. The best approach is a hybrid: start with synthetic data to get broad coverage and learn core skills, then fine-tune on a small set of real-world data to adapt to specific workflows. This combination often outperforms both synthetic-only and real-only baselines. The key is to design synthetic environments that closely mirror the real UI, navigation patterns, and error states. When the gap is small, the performance gains from synthetic data outweigh the minor domain shift risks.
How to design effective synthetic datasets for agents
Start with a clear task definition and collect representative real examples to understand boundaries.Build a synthetic environment that matches the real UI structure, controls, and navigation paths.Generate diverse trajectories that include normal flows, edge cases, and common errors.Use agents that already understand the UI to generate realistic interactions and reduce manual effort.Validate synthetic trajectories against a small set of real examples before full-scale training.Iterate quickly: update the synthetic environment as the real UI changes.
Synthetic data lets you train and evaluate AI agents at scale, with zero privacy concerns and dramatically lower labeling costs. The best results come from combining synthetic trajectories with a small amount of real-world data.
How Coasty fits into the synthetic data picture
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This gives Coasty a unique advantage: the synthetic datasets it produces reflect genuine user behavior, navigation patterns, and edge cases. Coasty's synthetic data service is custom and contact-led. Teams can discuss their specific workflows, UIs, and evaluation criteria, and Coasty builds tailored datasets that match their needs. There is no self-serve product or fixed package. The offering is designed around your use case, so you get data that fits directly into your training pipeline without unnecessary bulk or noise.
If you're building or evaluating AI agents, synthetic data is now a practical necessity. It solves the data scarcity problem, keeps costs down, and eliminates privacy risks. To explore how Coasty can help you build custom synthetic datasets for your specific workflows, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .