Why Synthetic Data Is Essential for Evaluating and Red Teaming AI Agents
Training and testing AI agents often stalls because teams can't get enough realistic interaction data. Real browser clicks, file operations, and system events are hard to capture. They can also leak sensitive information or violate privacy rules. Synthetic data solves both problems by generating safe, reproducible interaction scenarios that match real user behavior.
The Evaluation Bottleneck
Most organizations struggle with limited labeled datasets for agents. A recent analysis of open agent benchmarks found that only about 12% of test cases cover multi-step workflows using realistic browser and desktop actions. The rest rely on simplified tasks or static prompts. This gap makes it hard to detect subtle failures like incorrect file paths, broken navigation flows, or permission errors. Without enough coverage, a model can perform well on benchmarks but fail in production.
Red Teaming Requires Reproducible Edge Cases
Red teaming an AI agent means intentionally exploring dangerous or unusual scenarios. You want to verify that the agent refuses a harmful request, falls back gracefully, or escalates to a human safely. Real-world testing depends on finding edge cases by chance, which is slow and incomplete. Synthetic data lets you systematically generate thousands of edge cases: critical system actions, persistent authentication failures, UI state corruption, and more. Teams can measure failure rates across tens of thousands of scenarios in a single run.
Concrete Benefits of Synthetic Data
- ●Volume: Generate millions of agent trajectories in minutes, each with detailed context and outcomes.
- ●Safety: Remove PII, credentials, and confidential files before sharing datasets.
- ●Control: Define exact failure modes and success criteria for reproducible evaluation.
- ●Coverage: Target rare workflows such as multi-tab browsing, file uploads, and error recovery.
- ●Speed: Iterate on agent training and evaluation much faster than waiting for real user sessions.
The key insight: synthetic data doesn't replace real-world testing, it scales it. You can explore orders of magnitude more scenarios, surface hidden failure modes, and close the gap between benchmarks and production behavior.
How Coasty Fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This approach produces high-fidelity synthetic datasets and trajectories that mirror actual user behavior. Because the data is generated from real software environments, it reflects realistic browser navigation, file system interactions, and system events. Coasty offers a custom synthetic data service tailored to your specific use cases and evaluation needs. There is no self-serve platform or fixed pricing, each engagement is designed around your goals and handled through direct contact with the team.
Ready to evaluate and red team your AI agents at scale with realistic synthetic data? Book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call to discuss your requirements and explore how synthetic trajectories can improve your testing pipeline.