Evaluating AI agents is harder than testing a static model. Agents act in messy environments, make tool calls, and handle multi-step workflows. Real-world logs are gold, but they’re rare, biased, and sometimes unsafe to expose. Synthetic data offers a practical way to fill those gaps without the risks or costs of collecting production traces at scale.
The evaluation gap for agents
Most agent benchmarks rely on a handful of curated tasks. For example, the WebVoyager benchmark uses around 1,500 web navigation tasks. That’s a good starting point, but it leaves many failure modes unseen. When you scale to thousands of endpoints, backend changes, and new UI flows, a fixed dataset quickly becomes stale. Real logs accumulate slowly, and teams often cannot retroactively label them for safety or edge cases.
Why synthetic data helps here
Synthetic data lets teams systematically explore failure regions. You can generate thousands of edge cases, malicious inputs, unexpected tool outputs, broken APIs, or confusing UI states, without touching production. A 2024 study by McKinsey found that generative synthetic data can reduce labeling costs by up to 60% while maintaining statistical similarity to real data. For agents, this means you can simulate abuse attempts, rate limit spikes, or network failures at a fraction of the cost of real incident analysis.
Concrete techniques for agent red teaming
- Input poisoning: inject adversarial prompts or malformed JSON to test tool-call safety.
- State corruption: simulate partially retrieved data or conflicting context to see how agents handle inconsistencies.
- Environment simulation: mock API responses that trigger edge cases like 4xx errors, 5xx timeouts, or rate limits.
- Tool misuse scenarios: generate multi-step workflows where an agent repeatedly calls the same tool with invalid parameters.
- User intent drift: produce prompts that start benign but evolve into malicious requests, probing the agent’s refusal and escalation logic.
The key is control: you decide what to test and how the environment responds, then collect structured traces that can be automatically scored against safety policies.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers, capturing realistic interaction data. This gives you grounded synthetic datasets that reflect how people actually navigate tools and interfaces. The service is custom and contact-led, meaning you can define the use cases, workflows, and risk categories that matter for your agents. Coasty then produces synthetic trajectories and interaction logs tailored to your environment, so you can evaluate and red team with data that feels as real as production traces.
If you’re serious about agent safety and robustness, synthetic data should be part of your evaluation stack. To explore how Coasty can generate custom synthetic datasets for your agents, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .
Want to see this in action?
View Case Studies