Synthetic Data for Evaluating and Red Teaming AI Agents
Evaluating AI agents is hard. You need diverse tasks, edge cases, and malicious inputs, but real-world data is limited. You also cannot always test harmful behaviors on actual users. Synthetic data solves both problems. It lets you generate vast, realistic interaction scenarios so you can stress-test any agent before it goes live.
Why real data is a bottleneck for red teaming
Most organizations rely on a handful of real-world interactions to judge an agent. That snapshot is rarely representative. You might see 10 successful tasks and assume the agent is robust. But you are missing the failures that actually break production. Real data is also risky. Testing prompt injection, jailbreaks, or policy violations on real users exposes your product to harm and liability. And collecting enough of it takes time and budget.
What synthetic data actually looks like
Synthetic data is not just random text. It is structured interaction sequences that mimic real user behavior. For agents that perform computer use, this means clickable elements, form fills, navigation paths, and multimodal inputs. Think of it as a replayable movie of a user interacting with software. You can generate thousands of these trajectories in parallel, each with a different intent, error pattern, or malicious payload. The key is realism. If the synthetic clicks, scrolls, and delays look like a human, your red team gets useful signals.
Concrete benefits for red teaming
- ●Coverage: you can explore edge cases that rarely occur in production, such as weird UI layouts or rare error states.
- ●Safety: test adversarial inputs like prompt injections, data leakage prompts, or policy violations without exposing real users.
- ●Reproducibility: store and replay the exact sequence of actions to debug failures or compare model versions.
- ●Volume: generate millions of interaction traces to perform statistical analysis on failure rates, latency, and safety outcomes.
Synthetic data lets you red team agents at the same speed and scale you train them, exposing blind spots that real-world logs never show.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers. This gives it unique access to authentic interaction patterns, including how users navigate, click, fill forms, and handle error states. By observing these agents in action, Coasty can capture realistic trajectories and generate synthetic datasets tailored to your domain and agent type. The service is fully custom and contact-led. You talk to the Coasty data team about your specific evaluation goals, and they build the synthetic scenarios that match your environment and risk profile.
If you need to evaluate or red team AI agents more thoroughly, synthetic data is the practical next step. To explore how Coasty can help you build custom synthetic datasets for your agents, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .