The Data Flywheel: Synthetic Data for Self-Improving Agents
Companies are racing to ship autonomous agents that can use computers and browsers. The bottleneck is rarely the model architecture. It is the data. Real-world interaction data is expensive to collect, messy, and hard to label at scale. Synthetic data offers a way to generate fresh, realistic interaction sequences to train and evaluate agents without those constraints.
What makes synthetic data useful for agents
Synthetic data for agents is not just random text. It is sequences of actions, observations, and outcomes that mimic real computer use. When an agent needs to learn to navigate a web interface, click the right buttons, or fill out a form, synthetic interactions can provide thousands of examples that look and behave like the real thing. This matters because agents operate in dynamic environments where the cost of failure is high. Using synthetic trajectories lets you stress-test edge cases and rare workflows before a live deployment.
A numbers-based view of the impact
Research on reinforcement learning shows that synthetic environments can reduce the number of real-world interactions needed to reach a target performance by up to 70 percent. In practice, teams that switch to synthetic trajectories see faster convergence during training and more consistent behavior during evaluation. For example, one study found that agents trained on synthetic web navigation data reached 85 percent task success in just 20 percent of the episodes compared to 50 percent when trained only on real data. High-quality synthetic data is also useful for rare events. If an agent must handle a specific error message or a custom checkout flow that only occurs once per month, synthetic scenarios can generate hundreds of those cases quickly and cheaply.
Tradeoffs to keep in mind
- ●Realism gap: Synthetic sequences must closely match the real interface. If the layout or behavior diverges, the agent can learn incorrect mappings.
- ●Label maintenance: Synthetic data still needs accurate labels for rewards, steps, and outcomes. Poor labeling defeats the purpose.
- ●Coverage limits: Synthetic scenarios cannot perfectly replicate every real-world variation. They are best used to augment, not replace, real data.
- ●Quality control: The generation pipeline (agents, environments, and validators) must be robust to avoid drift over time.
Synthetic data can dramatically reduce the number of real-world interactions needed to reach a target performance, but only when the synthetic interactions accurately reflect the target environment.
How Coasty fits into the data flywheel
Coasty uses computer use agents that run on real desktops and browsers to capture realistic interaction data. This gives Coasty a unique advantage: the synthetic datasets it builds are grounded in actual user interfaces, workflows, and edge cases. The result is synthetic trajectories that closely mirror real-world behavior. Coasty offers a custom synthetic data service. There is no self-serve product or fixed pricing. You talk to the Coasty data team to define your requirements, and they build datasets tailored to your agents and workflows. This contact-led approach ensures the synthetic data aligns with your specific environments and success metrics.
If you are training or evaluating agents, synthetic data can close the gap between controlled training and unpredictable production environments. To explore what Coasty can build for your use case, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .