Self-improving agents, systems that learn from their own interactions and get better over time, promise a new layer of automation. But the feedback loop is only as strong as the data it runs on. Real-world interactions are expensive and risky, and they rarely cover edge cases. Without enough high-quality examples, agents stagnate. Synthetic data can close that gap.
Real-world data is not enough
Most teams rely on historical logs, user interactions, or scraped corpora. Those sources have hard limits. A typical production dataset might contain 10 million events, but the distribution is skewed toward common actions. Rare workflows, security-sensitive tasks, and ambiguous user intents appear only a few times. Agents trained on this data will be strong on the majority use case but fragile elsewhere.
Synthetic data adds coverage and control
Synthetic data lets you deliberately fill gaps. You can generate thousands of examples for rare workflows, adversarial inputs, or privacy-sensitive scenarios. A recent study on reinforcement learning agents showed that synthetic trajectories increased performance on out-of-distribution tasks by 23 percent compared to training solely on real logs. Synthetic data also provides cleaner labels because the actions, timing, and outcomes are known. This reduces annotation costs and simplifies evaluation.
Techniques that make synthetic data useful
- Task decomposition and procedural prompting to create multi-step workflows.
- Simulated environments that mirror real desktop and browser layouts.
- Iterative refinement: agents generate data, review it, and resample failures.
- Privacy masking and sanitization to ensure compliance with data regulations.
The data flywheel works when synthetic data feeds back into training, evaluation, and safety testing in continuous cycles.
How Coasty fits
Coasty operates computer use agents on real desktops and browsers, so it captures realistic interaction data. This gives teams a foundation of lifelike behaviors they can reuse. Coasty provides a custom synthetic data service: you specify your target tasks, domain, and constraints, and the team builds datasets and trajectories tailored to your needs. This is a contact-led process designed around your use case rather than a fixed product.
If you are building or evaluating self-improving agents, synthetic data should be part of your strategy. To explore how Coasty can help generate high-quality, custom datasets for your workflows, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call.
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