The Data Flywheel: Synthetic Data for Self-Improving Agents
Training AI agents to act on real computers and browsers is hard because good data is scarce. You cannot just scrape the open web, browser actions are messy, context-heavy, and often violate terms of service. Real click logs are noisy and expensive to label. The result is a training set that is too small, too noisy, or too narrow to teach an agent to handle novel situations.
Agents need interaction data, not just text
Language models are great at dialogue, but they struggle when the rubber meets the keyboard. An agent must understand UI layout, handle pop-ups, manage multi-step flows, and recover from unexpected errors. Real click logs capture this behavior, but they are fragmented and hard to extract. Most teams end up with isolated examples instead of complete trajectories.
Synthetic data closes the feedback loop
Synthetic data solves this by generating full interaction trajectories. You define a task, like filling a form or booking a flight, and a synthetic environment mimics a real desktop. An agent attempts the task, while an orchestrator records every UI state, action, and error. The result is a labeled sequence of steps that looks like real user behavior. Studies show synthetic trajectories can match or exceed real logs in coverage of edge cases, with up to 40% lower labeling costs. More importantly, you can generate millions of examples in parallel, far beyond what live users could provide.
Tradeoffs you should know
- ●Simulation fidelity: The closer the environment to production, the more useful the data. Overly simplified sims lead to poor transfer.
- ●Task diversity: Synthetic data must cover rare but critical flows, not just happy paths.
- ●Evaluation bias: Agents trained only on synthetic data can overfit to the simulator. Mixed datasets improve robustness.
The real power of synthetic data is not just speed. It is the ability to generate infinite, varied scenarios for agents to practice, iterate, and improve without risking production systems.
How Coasty fits into the flywheel
Coasty runs computer use agents on real desktops and browsers, not just in a sandbox. This means synthetic datasets capture realistic interaction dynamics, browser quirks, window management, and network variability. The Coasty team builds custom synthetic datasets on demand, tailored to your agents' tasks and environments. The service is custom and contact-led, so you work directly with the Coasty data team to define scope, quality, and integration with your training pipeline.
A data flywheel only spins if you can feed it high-quality trajectories at scale. If you want to accelerate your agent training with synthetic interaction data, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .