Synthetic Data for Evaluating and Red Teaming AI Agents
Most teams building AI agents hit the same wall: they need to test how their models behave in messy, risky situations, but they rarely have good labeled examples of those situations. Real logs are noisy, private, or incomplete. Collecting new examples often means exposing sensitive user data or waiting weeks for manual labeling. This blind spot makes it hard to catch edge cases and security failures early. Synthetic data solves the problem by letting you generate realistic scenarios on demand, without touching production traffic.
Why real logs fall short for agent red teaming
Real-world logs are great for understanding average performance, but they are terrible for probing failures. Most production systems are engineered to succeed most of the time. You rarely see examples of an agent accidentally deleting a file, revealing internal documentation, or getting stuck in infinite loops. Those failures show up in support tickets and outages, not in the logs you can query. Even when you do find them, the logs may contain PII, proprietary workflows, or require complex parsing to understand intent. Cleaning and anonymizing real data can take weeks, and you may still lack diversity across different tools, error states, or user personas.
Synthetic data gives you control over edge cases
With synthetic data, you define the conditions you want to test: a specific error message, a rare UI state, or a multi-step workflow that rarely occurs in production. You can generate thousands of conversations or interaction sequences that explore those conditions, all in a safe sandbox. For example, if your agent handles file uploads, you might create synthetic inputs that include malformed filenames, extremely large files, or executable attachments. You can then run your red teaming pipeline against those inputs and measure how the agent responds. Because the data is generated, you know exactly what scenario you are testing and can repeat it reliably. This reproducibility is critical for systematic regression testing and for demonstrating compliance with safety guidelines.
Measuring safety and robustness with synthetic benchmarks
A synthetic benchmark lets you quantify how often your agent handles risky inputs correctly. Consider an agent that processes customer support tickets. You can generate a synthetic dataset of 50,000 tickets that include malicious intent, jailbreak attempts, or requests for PII. When you compare the model's responses to a labeled ground truth, you can compute precision, recall, and false positive rates for each category of attack. One study showed that synthetic adversarial examples improved detection of prompt injection attacks by 30% compared to real-world logs alone. Another team used synthetic dialogues to train a safeguarding classifier, which then reduced unsafe completions in production by 22%. These numbers are not hypothetical; they reflect concrete improvements teams have achieved when they move from relying solely on real logs to a hybrid approach that includes synthetic test cases.
Techniques for building reliable synthetic scenarios
Template-based generation: Start with a few positive and negative examples and use template rules to vary wording, context, and format.Inversion of real logs: Take successful interactions and invert key steps, change a button click to a wrong action, swap a benign command for a harmful one.
Use synthetic data to systematically explore failure modes, measure safety metrics, and compare agent versions without exposing production data.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers, so it can capture realistic interaction data and produce synthetic datasets and trajectories for training and evaluating agents. Teams use Coasty to generate evaluation scenarios that mirror actual workflows, including rare error states, complex multi-step tasks, and subtle UI interactions. Because Coasty operates in controlled environments, you can safely probe edge cases and compliance risks without touching live systems. The service is custom and contact-led, meaning you work with the Coasty team to design datasets that match your specific use cases and safety requirements.
Red teaming AI agents is hard when you are limited to what happens in production. Synthetic data lets you create targeted scenarios that surface risks and guide safe deployments. If you want to explore how Coasty can help you build custom synthetic datasets for agent evaluation and red teaming, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .