The data flywheel: why synthetic data fuels self-improving agents
Building autonomous agents that can actually use a computer is harder than it looks. Most demos work on sanitized screenshots and toy tasks. When you move to real software and real workflows, the data quality collapses. Good labeled data becomes scarce. Real-world interactions are messy, expensive, and risky. Synthetic data offers a way out.
The agent data bottleneck
Agent evaluation is notoriously noisy. Human evaluators can only score a handful of trajectories per day. Automated benchmarks are often limited to a few thousand examples. That is not enough for model teams to reliably detect subtle improvements. When you train on imbalanced data, the model learns to overfit easy cases and ignore edge conditions. The result is confident-but-wrong agents that fail on the first real task.
Synthetic trajectories close the gap
Synthetic data means generating new interaction sequences that look like real user behavior. You specify the goal, the tools, and the constraints. An agent then attempts the task step by step. The system records clicks, keystrokes, error messages, and final outcomes. Because you control the conditions, you can create rare failure modes that never appear in production. One research project showed that synthetic trajectories reduced the variance in agent evaluation by 40 percent compared with human-only benchmarks. Another team used synthetic data to surface edge cases that were missing from their existing dataset, leading to a 15 percent boost in task success on unseen workflows.
Tradeoffs you need to know
- ●Synthetic trajectories can drift from real user behavior if you do not validate against live data.
- ●High-quality synthetic data requires an underlying model or agent that can generate plausible steps.
- ●Integration with existing evaluation pipelines is easier when you standardize on a common schema.
- ●You must balance coverage of rare scenarios against the cost of generating too much noise.
- ●Labeling quality is only as good as the definition of success you provide to the synthetic generation system.
The data flywheel works when synthetic data improves agent performance, which in turn produces more realistic interaction data that can be used to refine the synthetic generation process.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This allows them to produce synthetic datasets and trajectories that mirror genuine user behavior. Their approach is custom and contact-led, which means they work with you to design the workflows, tools, and constraints that matter for your agents. There is no fixed package or public price list. You start by talking to the Coasty data team to define what you need.
If you want to close the data gap for self-improving agents, synthetic data is a practical lever. Talk to the Coasty data team to explore how they can generate custom synthetic datasets for your use case: https://cal.com/coasty/coasty-data-call