Synthetic Data for Evaluating and Red Teaming AI Agents

The cost of limited evaluation data

Most agent teams rely on a handful of handcrafted test cases. A 2023 study on LLM evaluation found that models with access to 1,000 diverse prompts showed a 15% to 22% higher success rate on unseen benchmarks compared to those tested with only 100 prompts. More importantly, those high-performing models were significantly better at handling rare edge cases that real-world logs rarely surface.

Why synthetic data is useful for red teaming

Synthetic data lets you deliberately design failure modes, confusion, and edge cases that are hard to find in production. You can generate inputs that trigger specific policy violations, ambiguous instructions, or system-level errors. Because synthetic data is under your control, you can focus on the exact failure patterns you want to test. This is especially valuable for safety-critical domains like finance, healthcare, or customer support.

Techniques for high-quality synthetic test sets

• ●Use large language models to generate diverse prompts that probe edge cases.
• ●Combine multiple data sources to create realistic conversation histories and system states.
• ●Apply rule-based filters to remove trivial or clearly safe cases, keeping only the challenging ones.
• ●Iteratively refine prompts and filter criteria based on new failure patterns discovered during red teaming.
• ●Validate synthetic trajectories against known ground truth or expert review to catch systematic biases.

How Coasty fits

Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This gives teams a source of authentic trajectories that can be transformed into synthetic datasets for training and evaluating agents. Coasty’s offering is custom and contact-led: you talk to the team to define the use case and data requirements, then they build a tailored dataset that matches your specific needs.