Synthetic data for evaluating and red teaming AI agents
Training and evaluating AI agents requires high-quality interaction data. Real-world sessions are limited, expensive to label, and often risky to share. Synthetic data solves these constraints by generating realistic scenarios that mirror actual usage while keeping proprietary workflows and customer information private.
The evaluation gap for AI agents
Agent evaluations differ from single-turn LLM evaluations. An agent performs multi-step tasks across tools, APIs, and workflows. Running enough real sessions to statistically validate performance is impractical. A recent survey of AI teams found that fewer than 15% of agents are evaluated against more than 50 unique test cases, yet production systems often need hundreds or thousands.
Why synthetic data matters for red teaming
Red teaming an agent means exploring failure modes: unsafe tool usage, privacy violations, or policy breaches. Synthetic data lets you build targeted attack scenarios without touching live systems. You can generate malicious prompt variations, adversarial context, or edge cases that are rare in production but critical to test. One study showed that synthetically generated adversarial prompts uncovered 3x more policy violations than manually curated cases.
Key techniques for realistic synthetic tests
- ●Procedural generation of workflows: define rules and constraints, then let a generator create thousands of valid and invalid trajectories.
- ●Persona simulation: create user personas with specific goals, knowledge, and risk tolerance to probe different failure modes.
- ●Tool and API mocking: simulate responses, rate limits, and errors to test resilience without real dependencies.
- ●Privacy masking: replace real PII with synthetic identifiers to keep sensitive data out of test environments.
Use synthetic data to expand test coverage, protect confidential information, and speed up red teaming cycles.
How Coasty fits
Coasty operates computer use agents on real desktops and browsers, capturing realistic interaction data and trajectories. This approach yields synthetic datasets that reflect actual workflows, including edge cases and tool interactions. Teams use these synthetic datasets to train and evaluate their own agents. Coasty provides a custom synthetic data service, not a fixed product. To explore how synthetic data can support your agent evaluation and red teaming goals, book a data call with the Coasty data team.
Start building a robust evaluation and red teaming strategy with synthetic data. Book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call to discuss your requirements.