Why Synthetic Desktop and Browser Trajectories Are Critical for Agent Training
Building AI that can actually use a computer or browser is a different beast from text-only models. You need realistic trajectories: screen states, mouse movements, clicks, keystrokes, and the underlying DOM or UI trees. Real-world data is valuable but limited, risky, and expensive to collect at scale. Synthetic trajectories solve three problems at once: they let you generate rare scenarios, protect privacy, and accelerate evaluation.
Real-world interaction data is a bottleneck
Collecting high-quality desktop and browser interaction data is slow. You need access to live browsers or physical machines, consent from users, and careful logging of every event. Even then, you rarely see edge cases: complex workflows, failing scripts, or security alerts. One study of 10 million web requests found only 3 percent represented anything unusual. That means most real data is noisy and repetitive. Training on that alone makes models brittle. They learn the happy path and fail when things go sideways.
Synthetic trajectories give you control and coverage
Synthetic trajectories let you design the scenarios that matter. You can generate thousands of workflows: multi-step purchases, form submissions with validation errors, rare error states, and even security events. One synthetic data project produced 500k unique browser sessions covering 200 different user journeys, each with multiple variants of error conditions. That’s impossible to collect organically in any reasonable timeframe. Synthetic data also removes privacy concerns. You can scrub PII from the DOM and keep only structural elements. This lets you use real-world UI layouts without exposing real user identities.
Tradeoffs you have to manage
- ●Domain fidelity: synthetic trajectories must match the visual and functional quirks of real applications.
- ●Execution consistency: simulating browser layout engines and DOM updates is tricky.
- ●Label quality: you need ground-truth actions and outcomes to train or evaluate models.
- ●Scalability: generating and validating massive datasets requires robust pipelines and automation.
The sweet spot is synthetic data that is both realistic and controllable, with accurate labels and rich coverage of rare or risky scenarios.
How Coasty fits into the picture
Coasty operates computer use agents on real desktops and browsers. Those agents interact with live applications, capture raw interaction data, and produce high-fidelity trajectories. Because the agents run against real systems, the synthetic data they generate inherits genuine layout behaviors, timing quirks, and edge cases that pure simulations might miss. Coasty’s approach is custom and contact-led: you talk to the team to define the workflows, data volume, and target domains, then they build and deliver the synthetic dataset tailored to your needs.
If you’re building or evaluating AI that needs to operate on real computers and browsers, synthetic trajectories can dramatically improve your model’s robustness and coverage. To see how Coasty can provide custom synthetic data for your use case, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call.