Training AI agents to use computers is harder than you might expect. Most teams hit a wall around three problems: not enough good data, expensive labeling costs, and regulatory risk when working with real user credentials. This is where synthetic desktop and browser trajectories become a practical lever.
Real data is hard to get and expensive to label
High-quality computer use data is稀缺. To capture a genuine workflow, clicking through a SaaS dashboard, filling out a form, navigating a multi-step procurement process, you need humans or agents to repeatedly perform the same tasks. Even then, you need to record clicks, keystrokes, scroll positions, error messages, and any intermediate states. This takes time and money. A recent industry survey found that teams spend 40% of their budget on data labeling and preparation, and that 60% of those teams struggle to scale beyond simple web scraping.
Synthetic trajectories capture complexity without exposure
Synthetic trajectories let you generate diverse, realistic interactions without touching production systems. By simulating a browser or desktop environment and recording agent actions, you can create datasets that mirror the structure of real workflows. Think of it as a sandbox where each run is a new, controlled experiment. One common approach is to start with a small set of real interactions and then use reinforcement learning or rule-based policies to explore variations, changing form inputs, retrying different error paths, or simulating edge cases like network timeouts. This produces thousands of trajectories that are statistically similar to real usage but completely isolated from live systems.
Concrete benefits for agent evaluation
Synthetic data is also a powerful evaluation tool. You can design test suites that check for robustness, error recovery, and safety constraints. For example, you might generate 10,000 synthetic tasks where an agent must navigate a multi-step checkout flow, handle out-of-stock scenarios, and respect rate limits. Running your model against this suite gives you quantifiable metrics: success rate, average steps to completion, and failure modes. This is much harder to achieve with a handful of manual test cases. In practice, teams that incorporate synthetic test suites report a 30-50% reduction in production incidents caused by unexpected agent behavior.
Tradeoffs to keep in mind
- Domain gap: Synthetic environments must stay grounded in real workflows. If the tooling or UI evolves, your synthetic data can become stale.
- Cost of simulation: Setting up high-fidelity browser or desktop simulators requires engineering effort. Consider starting with a lightweight mock and iterating.
- Labeling vs. self-supervision: You can generate labels by replaying trajectories against known ground truth policies, but you need a reliable way to validate correctness at scale.
The most effective teams treat synthetic trajectories as a complement to real data, not a complete replacement. Use them where data scarcity or risk is highest, and validate your approach against a small but representative set of real interactions.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This enables the production of custom synthetic datasets and trajectories tailored to your specific workflows. The service is custom and contact-led, meaning you work directly with the team to define scope, coverage, and quality targets. There is no self-serve product or fixed package; instead, you discuss your needs and get a proposal based on your use case.
If you need high-quality synthetic desktop and browser trajectories for training or evaluating your agents, the next step is to book a data call with the Coasty data team. Schedule your call at https://cal.com/coasty/coasty-data-call to explore how realistic interaction data can accelerate your agent development.
Want to see this in action?
View Case Studies