Training an LLM agent means teaching it to act on a computer, not just chat. The real problem: you need massive, diverse interaction logs. Real logs are expensive, noisy, and sometimes risky (PII, sensitive workflows). Synthetic data can fill that gap.
Why synthetic agents matter
Synthetic agents let you generate thousands of realistic sessions in hours. You can craft edge cases, safety flows, and multi-step workflows that rarely happen in production. A 2024 study on agent evaluation found that synthetic data improved robustness by 23% on unseen scenarios when combined with a small slice of real logged data. The key is realistic interaction signals: mouse movements, clicks, screen states, and natural language responses. Pure text prompts aren't enough.
Techniques for high-quality synthetic agent data
- Run autonomous agents on controlled environments (sandboxed desktops or browsers).
- Inject domain-specific goals and constraints to generate varied behaviors.
- Use rule-based or LLM-driven supervisors to validate actions and produce correct labels.
- Combine synthetic sessions with a small, curated set of real logs to keep ground truth accurate.
- Filter for replayability: sessions that can be reproduced reliably for evaluation.
High-quality synthetic agent data is realistic, scalable, and safe. It lets you train and evaluate agents on edge cases and workflows that are hard to capture from production alone.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers. This lets it capture realistic interaction data and produce custom synthetic datasets and trajectories for training and evaluating agents and models. The service is custom and contact-led. There is no self-serve product or fixed package. If you need synthetic data tailored to your agent workflows, the Coasty data team can design and deliver a dataset that matches your needs.
Ready to build better agents with realistic synthetic data? Book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .
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