Synthetic Desktop and Browser Trajectories for Agent Training
Training AI agents to use computers and browsers is expensive and risky. Real-world clicks and keystrokes are hard to collect at scale. You also cannot safely replay production sessions with sensitive data. Synthetic trajectories let you generate vast amounts of realistic interaction data in controlled environments.
Why desktop and browser trajectories matter for agents
Modern AI agents need to perform multi-step tasks across apps and websites. They must understand layouts, click the right buttons, fill out forms, and handle errors. This requires data that shows how a human would interact with a real UI. Static screenshots or text descriptions are not enough. Agents need sequences of actions, clicks, scrolls, text inputs, and navigation, that reflect how users actually move through a desktop or browser.
Real numbers behind synthetic trajectory generation
Studies show that synthetic training data can match or exceed the performance of models trained on smaller real datasets. For example, a 2023 research project generated 1 million synthetic navigation trajectories and trained an agent that achieved similar task success rates to a model trained on 500,000 human trajectories. Synthetic data also reduces labeling costs by up to 90% when combined with automated annotation pipelines. The key is to generate trajectories that closely resemble how humans behave, including occasional mistakes, backtracking, and adaptive behavior.
Key tradeoffs and techniques
- ●Quality vs scale: Higher-fidelity simulations cost more but produce more reliable training signals.
- ●Domain coverage: Synthetic environments must vary widely to teach agents to handle different layouts and workflows.
- ●Error realism: Adding realistic failure modes helps agents recover from mistakes in production.
- ●Privacy safety: Synthetic data is fully sanitized, so you never expose real user credentials or internal data.
- ●Evaluation alignment: Use synthetic trajectories to benchmark agents in controlled settings before real-world deployment.
Synthetic trajectories let you train and evaluate agents at scale without the safety or cost issues of real-world data.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This approach produces rich, verifiable trajectories that reflect how people actually interact with software and websites. Coasty can turn these observations into custom synthetic datasets tailored to your agent’s use cases. The service is custom and contact-led, meaning you work directly with the team to design the data you need.
If you need realistic synthetic trajectories for agent training, book a data call with the Coasty data team to discuss your requirements.