The data flywheel: synthetic data for self-improving agents
Training AI agents is harder than training a static model. You need diverse, realistic interaction data. Real-world clicks, windows, and browser actions are expensive to capture and can expose sensitive information. Synthetic data offers a way to generate large volumes of safe, controllable examples without touching live systems.
Why agents need more than text labels
Text classification and summarization rely mostly on labeled documents. Agents must understand visual layouts, navigate graphical user interfaces, and handle multi-step workflows. They need examples of mouse movements, window layouts, error dialogs, and menu structures. A single screenshot rarely captures enough of that context. Real data from production is scarce, expensive to label, and often noisy.
The data flywheel in action
- ●Start with a small set of real user trajectories.
- ●Use them to teach a computer use agent how to navigate.
- ●The agent simulates additional interactions in a controlled environment.
- ●Collect these simulated trajectories as synthetic training data.
- ●Add them to the training mix to improve the agent's policy.
- ●A stronger policy generates higher-quality simulations, and the cycle repeats.
Concrete metrics that change with synthetic data
Benchmarks for computer use agents show real gains when synthetic data is added. Labs using simulated environments for reinforcement learning report up to 2.5x faster convergence on navigation tasks. Teams that combine 5% real trajectories with 95% synthetic trajectories see a 30% reduction in failure rate on complex workflows. Synthetic data also cuts labeling costs dramatically: labeling a single complex browser workflow can take hours of manual work, while generating a synthetic version takes minutes.
A data flywheel means your agent gets better, which produces more useful synthetic examples, which makes the agent even better. You can iterate on policies and environments continuously without waiting for new real users or risking production traffic.
How Coasty fits into the flywheel
Coasty runs computer use agents on real desktops and browsers. This lets it capture highly realistic interaction data and generate synthetic datasets that mirror real-world use. The service is custom and contact-led. You discuss your target workflows, evaluation metrics, and constraints with the Coasty data team. They build or adapt a synthetic dataset that matches your environment and quality requirements. The result is synthetic trajectories you can plug directly into your training or evaluation pipelines.
You do not have to build simulation engines or label workflows by hand. Coasty can help you spin up the synthetic side of your data flywheel. Book a data call with the Coasty data team to discuss your use case and see how synthetic data can accelerate your agent development at https://cal.com/coasty/coasty-data-call.