Synthetic Data vs Real Data for Training AI Agents
Most teams training or evaluating AI agents hit the same wall: they need lots of labeled, realistic interaction data, but real-world data is scarce, expensive, and risky. You can’t scrape everything, and manual labeling is a bottleneck. Synthetic data can help, but it comes with its own tradeoffs. Here’s what you need to know.
The real cost of real data
Getting high-quality, labeled interaction data for agents is expensive. A 2024 study on AI agent training found that manual data labeling can cost between $5 and $30 per labeled interaction, depending on complexity and domain. For large-scale deployments, this quickly becomes the dominant line item. Real data also carries risk: you must handle privacy, compliance, and reproduction issues. Once you delete a real session, you can’t get it back. Synthetic data lets you generate unlimited, reproducible examples at a predictable marginal cost.
Where synthetic data helps most
Synthetic data shines when you need to cover rare edge cases or test scenarios that are hard to trigger in production. For example, an AI agent that needs to handle complex multi-step workflows in a regulated environment can benefit from synthetic workflows that simulate compliance checks, error states, and retry loops. In benchmarks, synthetic trajectories can expand the diversity of test inputs by 2x, 5x, exposing more edge cases than a small real-world dataset. This is especially useful for evaluation: you can generate thousands of consistent scenarios to measure reliability and safety.
Key tradeoffs to watch
- ●Realism vs. control: synthetic trajectories are highly controllable but may miss subtle user behaviors and unexpected edge cases.
- ●Domain knowledge: the quality of synthetic data depends on how well you model the underlying task and environment.
- ●Labeling effort: synthetic data reduces manual labeling but may still require expert review or validation.
- ●Overfitting risk: if synthetic data is too uniform, the agent may overfit to artificial patterns and struggle with real-world variability.
The most effective teams use synthetic data to expand diversity and evaluation coverage, while still validating against a small but representative set of real examples.
How Coasty fits
Coasty captures realistic interaction data by running computer use agents on real desktops and browsers. This lets teams obtain authentic user journeys, UI flows, and error states that are hard to reproduce artificially. From that foundation, Coasty can produce custom synthetic datasets and trajectories tailored to your agent’s domain. The offering is a custom, contact-led service: you talk to the Coasty data team to define your requirements, scope, and use cases, and they build the solution around your needs.
If you’re building or evaluating AI agents and want to increase your data coverage without sacrificing quality or incurring massive labeling costs, the next step is to talk to the Coasty data team. Book a data call at https://cal.com/coasty/coasty-data-call to explore how synthetic data can support your specific use case.