Why Synthetic Data is Critical for Evaluating and Red Teaming AI Agents
Building reliable AI agents is hard because real-world data is sparse, noisy, and expensive to label. When you evaluate an agent on production traffic, you see what works but you miss edge cases and failure modes. Synthetic data solves this by letting you generate thousands of edge-case scenarios, adversarial prompts, and complex workflows on demand.
Real-world costs of manual agent evaluation
Manual red teaming scales poorly. A study by a leading AI safety lab found that manually testing a 20-step browser task required an average of 4.7 hours per test case and captured only 12 distinct failure modes out of 89 possible issues. The cost per failure mode was about $520 in researcher time. Synthetic data reduces this by an order of magnitude: an automated benchmark on 10,000 synthetically generated tasks found 76 unique failure modes with 12% of cases flagged as critical, all generated in under 6 hours at a fraction of human effort.
What synthetic data actually improves
- ●Coverage: generate rare workflows and long-horizon tasks that rarely occur in production.
- ●Safety: create adversarial prompts and jailbreak attempts without exposing production systems.
- ●Speed: iterate on evaluation pipelines in minutes rather than weeks of data collection.
- ●Privacy: avoid exposing real customer data or PII while still testing against realistic inputs.
- ●Control: vary parameters like task complexity, error rates, and latency to stress-test the agent.
The core insight: synthetic data does not replace real-world performance; it reveals hidden failure modes that real data never shows.
How to build effective synthetic datasets
Start with a clear evaluation rubric. Define success criteria, error types, and safety boundaries before generating data. Use a two-layer generation approach: first create high-level task specifications, then instantiate them with realistic tool calls, system states, and user inputs. For agent benchmarks, combine synthetic tasks with a small set of real interactions to calibrate performance estimates. Maintain a versioned dataset library so you can re-run evaluations as models improve.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This allows it to produce synthetic datasets that reflect how actual users navigate tools and applications. The offering is a custom, contact-led service: you work directly with the Coasty data team to define your evaluation scenarios, data coverage, and quality requirements. There is no self-serve platform or fixed package. The goal is to deliver datasets that match your specific benchmarks and safety constraints.
If you need synthetic data for agent evaluation or red teaming, the next step is to talk to the Coasty data team. Book a data call at https://cal.com/coasty/coasty-data-call to discuss your use case and explore a custom data solution.