Synthetic Desktop and Browser Trajectories for Agent Training
Most teams struggle to get enough realistic interaction data. Real-world logs are messy, full of noise, and risky to expose. Synthetic trajectories let you generate clean, verifiable sequences of clicks, inputs, and navigation events that mirror how humans use desktop tools and browsers. This post explains what desktop and browser trajectories are, why they matter, and how teams use them to train and evaluate agents at scale.
What are desktop and browser trajectories?
A trajectory is a sequence of actions over time. For desktop and browser tasks, this means clicks, keystrokes, scrolls, and navigation steps captured while a human completes a goal. The data includes timestamps, element states, and intermediate steps that show how a user arrived at the final result. Synthetic trajectories are generated rather than logged from a live user session. They must accurately reflect the complexity of real interactions, including text inputs, form completions, and multi-step workflows.
Why synthetic desktop data matters
Real logs are expensive to collect and hard to structure. They often contain sensitive information and require strict compliance checks. Synthetic trajectories solve these problems by letting teams create reproducible, privacy-safe datasets. They also enable rapid iteration: you can generate new scenarios, edge cases, and failure modes without waiting for new user sessions. This matters for reliability tests and regression checks. Teams using synthetic data report faster experimentation cycles and better coverage of rare tasks. For example, a fintech team generated synthetic onboarding flows with varied error states and used them to stress-test their agent before going live.
Common techniques and tradeoffs
- ●Behavioral cloning: models learn from logged human trajectories and generate new sequences based on learned patterns.
- ●Reward modeling: reinforcement learning agents explore a simulated environment and collect trajectories that maximize a reward function.
- ●Hybrid generation: combine real interaction snippets with synthetic steps to preserve realistic patterns while expanding coverage.
- ●Privacy injection: mask or replace sensitive fields in synthetic data so it can be shared or used in open benchmarks.
- ●Benchmark alignment: design trajectories that mirror public benchmarks (e.g., WebShop, BrowserGym) to enable fair comparisons.
The key insight: synthetic trajectories must preserve the complexity and variability of real human behavior. Generic click-and-type scripts fail because they miss UI context, error states, and multi-step reasoning. High-quality synthetic data requires agents that understand the interface and can reproduce realistic workflows.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This allows the team to produce synthetic datasets and trajectories that reflect real-world usage patterns. Coasty offers a custom synthetic data service tailored to your specific workflows and benchmarks. It is a contact-led process, meaning you work directly with the data team to define requirements and scope. There is no self-service platform or fixed pricing. The focus is on delivering high-quality, realistic trajectories that you can use for training and evaluation.
If you need synthetic desktop and browser trajectories for agent training, the best next step is to book a data call with the Coasty team. They can help you design a custom dataset that matches your benchmarks and use cases. Book a data call at https://cal.com/coasty/coasty-data-call to start the conversation.