Most teams building AI agents hit a wall: they need more labeled interaction data to improve performance, but collecting it on real desktops is slow, risky, or impossible. Real data is valuable but often scarce. Synthetic data solves this by generating realistic scenarios on demand, letting you expand training sets, stress-test agents, and close the loop on self-improvement without exposing production environments.
Why the data flywheel matters
The data flywheel is the cycle where better models attract more users, who generate more real-world interactions, which feed back into model training and further improve performance. Synthetic data accelerates every stage of this loop. Teams can spin up thousands of realistic user journeys, edge cases, and multi-step tasks without waiting for real users to perform them. This means faster iteration cycles, more robust evaluations, and the ability to cover rare but critical scenarios that would otherwise take years to surface naturally.
Concrete benefits with real numbers
- Training efficiency: Synthetic data can reduce training time by 20, 40 percent when used to augment real datasets, as models see more diverse interactions early in the learning process.
- Scenario coverage: Teams can generate 10x more edge-case scenarios in a week than they could collect from production logs, enabling comprehensive stress testing before deployment.
- Cost per labeled example: Synthetic trajectories can drop the cost per labeled interaction to a few cents, compared to hundreds of dollars for human-labeled real-world sessions.
- Risk reduction: Using synthetic data for pre-training and evaluation removes the risk of exposing sensitive user actions or proprietary workflows in early iterations.
The key insight: synthetic data doesn't replace real data. It replaces the need for slow, expensive, or risky data collection by providing large-scale, high-fidelity simulations that can be used for both training and rigorous evaluation.
How teams build the flywheel
Modern approaches treat synthetic data as a reusable asset. Teams design reusable task templates, like filing a tax return, updating a CRM, or navigating a SaaS dashboard, then generate thousands of variations with different user behaviors, error states, and system responses. These trajectories are used to train models on a wide range of interactions, then evaluated against held-out real-world logs to measure generalization. As the model improves, it handles more complex workflows, which produces higher-quality real interactions, which in turn refine the synthetic generators. The flywheel spins faster with each cycle.
Common pitfalls and how to avoid them
- Over-simplifying tasks: Synthetic data that doesn't match the complexity of real workflows leads to brittle models that fail in production. Use realistic multi-step sequences and varied user intents.
- Neglecting domain constraints: Failing to model edge cases, error states, or system limits can make synthetic data less useful for real-world evaluation.
- Treating synthetic data as a one-off: The best results come from treating synthetic data as a continuous input stream, updated as models and environments evolve.
The most effective teams treat synthetic data as an integral part of their evaluation and training pipeline, not a standalone project. It requires intentional design of task templates, feedback from real-world performance, and ongoing refinement of synthetic scenarios.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers, capturing realistic interaction data that reflects how humans actually work. This allows Coasty to produce synthetic datasets and trajectories tailored to your workflows, domain, and edge cases. Coasty's offering is a custom synthetic data service that you collaborate on with the team to match your needs. It is contact-led, meaning you start by discussing your use case and data requirements before any solution is built.
If you're building agents that need more data, better evaluations, and a sustainable data flywheel, start the conversation with Coasty. Book a data call with the Coasty data team to explore how custom synthetic data can accelerate your agent development cycle at https://cal.com/coasty/coasty-data-call.
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