Why Synthetic Data Is Critical for Evaluating and Red Teaming AI Agents
You cannot evaluate an AI agent on the handful of demos you built. Real usage is messy, biased, and sparse. Public benchmarks capture only a slice of behavior. And running thousands of agents live exposes your system to real-world risks. Synthetic data solves this by letting you generate millions of realistic interactions, corner cases, and adversarial prompts on demand.
The problem with current evaluation stacks
Most teams rely on a mix of static benchmarks and a few manual test cases. Static benchmarks are narrow. They rarely cover the full breadth of tools, workflows, and error states the agent might hit. Manual tests are too slow to iterate. When you try to scale to thousands of test scenarios, you quickly hit three walls: cost, safety, and coverage. A recent survey of 23 AI engineering teams found that 71% struggle to generate enough diverse test cases, and 64% cite safety concerns as the main blocker for automated red teaming at scale.
What makes synthetic data useful for agents
Synthetic data for agents is not just about text. It is about full interaction trajectories: user intent, tool calls, UI clicks, error states, and system responses. You can generate these at scale by simulating realistic workflows or by running agents on synthetic environments that mirror production UIs and APIs. For example, a fintech agent might need to handle dozens of edge cases: invalid account numbers, network timeouts, consent revocation, and mixed-currency transactions. Synthetic data can produce thousands of variations of these scenarios in minutes, each with a full trace of actions and outcomes.
Concrete benefits with real numbers
- ●Test coverage: Teams using synthetic workflows report 5x more unique scenarios compared with manual testing.
- ●Cost: Synthetic trajectories cost roughly 10% of the compute needed to run live red teaming at similar scale.
- ●Safety: Synthetic environments let you inject adversarial inputs and malicious tool calls without touching production systems.
- ●Speed: New edge cases can be generated and evaluated within hours, not days or weeks.
Synthetic data turns red teaming from a periodic, manual chore into a continuous, automated capability that keeps pace with model updates and new features.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers, capturing realistic interaction data and trajectories. This lets you produce custom synthetic datasets that reflect your actual workflows, tools, and UI patterns. The service is custom and contact-led: you discuss your evaluation goals, data requirements, and constraints with the Coasty team, and they build the right synthetic scenarios for you.
To explore how synthetic data can improve your agent evaluation and red teaming process, book a data call with the Coasty team at https://cal.com/coasty/coasty-data-call.