Training AI models is hard. Testing them is harder. Most organizations rely on a handful of sandbox environments or static benchmarks. Those capture only a slice of reality. When agents interact with real software, they leak sensitive info, break workflows, or behave in ways the model never saw in training. You need more tests, more edge cases, and more diversity, but real interaction data is expensive, risky, and hard to gather at scale.
Why static benchmarks fall short
Static benchmarks measure performance on a fixed set of prompts. They are useful for quick comparisons, but they do not reflect the messy, dynamic environment agents actually operate in. Users click, type, and navigate unpredictably. They make typos, cancel tasks, or switch contexts mid-operation. Benchmarks can miss these subtle failure modes. A model might ace a structured test but crash when a user changes a field name or pastes an unexpected error message. You need data that mimics this variability.
Real tradeoffs: synthetic data vs real data
- Real data captures genuine user behavior and unexpected edge cases, but collecting it at scale is slow, legally complex, and often impractical.
- Synthetic data eliminates privacy and compliance risks by using controlled environments and anonymized traces.
- Synthetic datasets can grow infinitely, letting you generate thousands of unique test scenarios, including rare failure cases that never occurred in production.
- The main limitation is fidelity. Synthetic data may not fully reproduce the nuances of real user interactions, especially when users introduce randomness or domain-specific quirks.
Techniques that work in practice
To build effective synthetic datasets, teams combine three approaches. First, use controlled environments like sandboxes, VMs, or containerized desktops where agents can safely interact. Second, record both the agent's actions and the system's responses, including error messages, timeouts, and UI state changes. Third, augment the data with variations such as different user personas, error conditions, and workflow interruptions. A well-designed synthetic dataset can include 50,000+ unique interaction sequences, each capturing a distinct failure pattern. This scale is impossible to achieve with manual data collection.
Synthetic data alone is not a silver bullet. The most effective evaluation pipelines mix curated real-world traces with large, diverse synthetic datasets. This hybrid approach captures rare, high-stakes failures while keeping testing fast and scalable.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture authentic interaction data. This realistic telemetry can be used to generate custom synthetic datasets tailored to your applications and risk profiles. Because the service is custom and contact-led, you work directly with the Coasty data team to define requirements, scenarios, and quality criteria. There is no fixed pricing or generic product, everything is shaped around your specific use case.
If you want to evaluate and red team AI agents with data that reflects real-world complexity, consider a custom synthetic data approach. Book a data call with the Coasty data team to explore how realistic interaction data can strengthen your evaluation pipeline at https://cal.com/coasty/coasty-data-call.
Want to see this in action?
View Case Studies