Synthetic Data for Evaluating and Red Teaming AI Agents

Why synthetic data helps with evaluation and red teaming

When you red team an AI agent, you want to expose it to edge cases, adversarial prompts, and rare workflows. With real user logs you often lack enough examples of these rare events. Synthetic data lets you generate thousands of them in parallel. You can create custom scenarios like SQL injection attempts, misleading document layouts, or multi-step browser flows that your agents rarely encounter in production. This coverage is hard to achieve by just collecting more real data.

Real tradeoffs and techniques

• ●Quality vs quantity: Synthetic data is only as good as the rules and scenarios you define. Poorly designed prompts or environments can lead to useless test cases.
• ●Coverage: You can surface rare or risky scenarios that real data might never show, but you must validate that the synthetic scenarios actually reflect real-world behavior.
• ●Cost: Generating synthetic data upfront can be cheaper than collecting and labeling large real-world datasets, especially for specialized domains.
• ●Bias amplification: If your synthetic scenarios inherit biases from their generation rules, your red team results may skew toward those patterns.
• ●Integration: Synthetic scenarios must be integrated into your evaluation pipelines. Mapping synthetic actions to your own metrics and guardrails is non-trivial.

How Coasty fits

Coasty runs computer use agents on real desktops and browsers, capturing realistic interaction data and trajectories. This allows Coasty to produce custom synthetic datasets and interaction flows tailored for training and evaluating agents and models. The service is custom and contact-led. There is no public price list or fixed package. To explore what is possible for your use case, book a data call with the Coasty data team.