Scaling Synthetic Data Generation Without Scaling Headcount
Most teams hit a wall when trying to expand their AI training or evaluation data: budgets, privacy rules, or labeling costs block them. Real data is often risky or expensive. Synthetic data can bypass those limits, but it is not a magic bullet. You need a realistic view of what it can and cannot do.
The data gap is real
A common benchmark: teams often need thousands of hours of labeled interaction data for end-to-end agent evaluation. A 2024 study on AI agents found that 63% of commercial projects struggle to obtain enough high-quality interaction data for robust testing. Real-world data collection can take months and cost hundreds of thousands of dollars when you factor in labeling, compliance, and domain experts. Synthetic data lets you generate that volume on demand, but the biggest obstacle is not volume, it is realism and alignment with your specific workflows.
Quality vs. quantity: what the numbers show
Several recent evaluations compare synthetic trajectories to human-labeled data for computer-use agents. In one 2024 benchmark, synthetic trajectories achieved 89% of the performance of human-labeled data on a standard browser automation task, with a 40% reduction in cost per hour of training. Another study on code agents found that synthetic test cases improved pass rates by 12% when used to augment a small human-coded test set. These gains come from scaling the variety of edge cases and rare interactions that a small human team could never cover.
Key tradeoffs
- ●Realism depends on the generation method: models that mimic user actions can capture realistic navigation patterns, but they may miss rare or creative workflows.
- ●Alignment with domain logic is critical: synthetic sequences must respect business rules, UI constraints, and security policies, otherwise they create misleading training signals.
- ●Validation overhead: synthetic data still needs some human review or automated checks to ensure it matches production behavior.
- ●Data drift: if your product changes, you must regenerate or update synthetic data to stay aligned.
The most effective strategy is to use synthetic data to expand coverage and test diversity, while keeping a core set of real-labeled examples for high-stakes validation.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This approach lets teams generate synthetic datasets that closely mirror actual user workflows and edge cases. Coasty’s offering is a custom synthetic data service. You work with the team to define your use case, scope, and quality requirements, and they produce tailored trajectories and datasets. There is no self-serve dashboard or fixed package. The focus is on a contact-led, custom engagement that matches your specific needs.
Don’t let data scarcity slow your AI projects. Book a data call with the Coasty data team to explore how custom synthetic datasets can help you scale training and evaluation without adding headcount: https://cal.com/coasty/coasty-data-call