Synthetic Data vs Real Data for Training AI Agents
You cannot build reliable AI agents with empty training sets. Real interaction data is the gold standard, but it is also hard to get, risky to share, and expensive to label. Many teams hit a wall right after they realize they do not have enough high-quality examples.
Why real data is hard to scale
Gathering real desktop or browser interaction data requires access to live environments, permissions, and careful privacy handling. A typical enterprise might need thousands of hours of logged sessions just to cover a few key workflows. Labeling those sessions adds another layer of cost and complexity. When teams try to scale, they often discover that real data becomes a bottleneck.
What synthetic data actually does
Synthetic data is generated by simulating realistic interactions with software or by replaying anonymized trajectories. The key is that the patterns, errors, and edge cases mirror real use. Studies show that well-designed synthetic datasets can reduce training time by 30, 50 percent while maintaining comparable model performance on a held-out test set. The downside is that bad generation leads to noisy data and biased models.
Key tradeoffs you should track
- ●Real data captures unanticipated behaviors that are hard to simulate.
- ●Synthetic data lets you control the distribution and fill gaps in coverage.
- ●Real data requires permissions, contracts, and strict privacy measures.
- ●Synthetic data can be created repeatedly without additional legal overhead.
- ●Both types benefit from high-quality labeling and rigorous evaluation.
Synthetic data is not a magic replacement. It works best when combined with a real-world dataset to cover the full spectrum of interactions.
How Coasty fits into the picture
Coasty runs computer use agents on real desktops and browsers. This lets the team capture realistic interaction data and produce synthetic datasets and trajectories that reflect actual user behavior. The service is custom and contact-led: you talk to the Coasty data team about your specific workflows, constraints, and targets. There is no public pricing or fixed package. The focus is on delivering synthetic data that matches your real-world environment.
If you want to speed up agent training, improve evaluation, and reduce reliance on fragile real-world data, start by exploring what synthetic data can do for your use case. Book a data call with the Coasty data team to discuss your project and see how custom synthetic datasets can fit into your pipeline: https://cal.com/coasty/coasty-data-call