You cannot build reliable autonomous agents with insufficient data. Real-world interactions are messy, expensive to label, and hard to reproduce. Synthetic data offers a way to generate high-quality, realistic training and evaluation data at scale. This creates a data flywheel where better agents improve data quality, and better data improves agents.
The problem with real data for agents
Agents need sequences of actions, user intents, and environmental feedback. Real datasets are hard to come by. Human-labeled agent trajectories take weeks to curate and cost hundreds of dollars per hour. Data collected from live users introduces privacy risks and cannot be safely reused across teams. A single production outage can wipe out months of labeled interaction data. This makes it difficult to iterate quickly or train agents for rare but critical scenarios.
Why synthetic data accelerates the data flywheel
Synthetic data lets you generate millions of trajectories in days. You can design edge cases, rare intents, and failure modes that never occurred in production. Early experiments with synthetic trajectories show a 20, 40 percent lift in success rates on benchmark tasks after just a few thousand synthetic examples. Agents trained on synthetic data reach comparable performance to those trained on raw logs after fewer total examples, reducing compute and annotation time. Synthetic data also enables safe evaluation. You can run adversarial tests and safety checks against a controlled dataset without risking real users or production systems.
Key techniques to make synthetic data realistic
- Simulate realistic user intent distributions, not just the most common cases.
- Include noise, interruptions, and conflicting goals to mimic real workflows.
- Capture tool outputs, error states, and multi-step reasoning explicitly.
- Use human oversight to validate high-value synthetic examples.
A strong data flywheel needs high-fidelity synthetic trajectories that mirror how agents actually interact with tools, apps, and the web.
How Coasty fits into the data flywheel
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This approach produces synthetic datasets that reflect how agents navigate applications, fill forms, and complete multi-step workflows. The output is actionable trajectories that teams can use to train, fine-tune, and evaluate their own agents. Coasty’s service is custom and contact-led, meaning you work directly with their team to design datasets that match your specific use cases, tools, and evaluation metrics. There is no self-serve product or fixed package. The focus is on delivering the right synthetic data for your agents.
You do not need more raw logs. You need better data that reflects how agents actually work. If you want to build a data flywheel for your agents, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call to explore how realistic synthetic trajectories can accelerate your training and evaluation pipeline.
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