Training AI agents or models on rare events is hard. Real data shows up at the rate a system produces it. When you need specific failures, adversarial inputs, or niche workflows, you might wait years for enough examples. You might also shy away from using sensitive or risky examples. Synthetic data solves these problems by generating realistic scenarios on demand.
The cost of waiting for rare events
Rare events do not show up often enough to build robust benchmarks. A typical call center dataset might have 1 in 10,000 interactions involving a sophisticated fraud attempt. A cybersecurity platform might see 0.1 percent of sessions include an advanced exploit. When you train on such sparse data, the model learns the common case and ignores the rest. Precision and recall on edge cases suffer. Benchmarks that rely on few examples are noisy and unstable.
Realism matters, not just volume
Not every synthetic dataset works well. You need interactions that look and feel real. Random text or simple geometric shapes do not capture the complexity of human workflows. When synthetic examples are obviously fake, models overfit to superficial patterns. Research shows that synthetic data must mirror the distribution of real interactions to transfer learning effectively. The best synthetic datasets preserve realism in actions, context, and error states.
Controllable scenarios
With synthetic data you control every variable. You can create adversarial inputs, misaligned prompts, or subtle UI bugs. You can isolate specific failure modes like misinterpretation of ambiguous instructions. This control lets you build comprehensive test suites that cover corner cases you would never see in production data. You can also vary difficulty levels to stress-test your system progressively.
Tradeoffs to watch
- Distribution mismatch: synthetic data must reflect real-world distributions, or the model will not generalize.
- Generation quality: poorly crafted synthetic interactions can introduce bias or unrealistic behaviors.
- Labeling overhead: you still need accurate ground truth, especially for complex multi-step tasks.
- Regulatory limits: some data types require explicit consent or anonymization; synthetic data can help, but legal review remains important.
The bottom line: synthetic data does not replace real data. It complements it by providing a scalable, controllable source of rare and edge-case examples that are realistic enough for training and evaluation.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers. These agents interact with actual applications and capture realistic workflows, including edge cases and error states. Using that data, Coasty can produce custom synthetic datasets and trajectories for your specific use case. The service is custom and contact-led: you talk to the Coasty team to define your requirements and receive a tailored solution.
Build robust benchmarks for rare events and edge cases with synthetic data. The Coasty data team can help you design and generate the right examples for your AI projects. Book a data call with them at https://cal.com/coasty/coasty-data-call .
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