Most teams building autonomous agents hit the same bottleneck: they need millions of realistic interaction samples but cannot generate them manually or cheaply. Real-world data is expensive, slow, and often incomplete. Synthetic data generation can fill that gap, but only if it is done efficiently. This is how you expand your dataset without expanding your headcount.
The Real Cost of Collecting Interaction Data
Collecting high-quality computer-use data usually means deploying engineers to record sessions on real desktops and browsers. A 2023 survey of AI labs showed that manually labeling a single multimodal task can take 30 minutes to an hour. Even with automation, the process requires ongoing human oversight to tag intents, correct errors, and validate edge cases. For a single agent with 100k training trajectories, that workload easily exceeds two full-time engineers per month. Scaling to millions of samples quickly becomes a human resource problem, not a compute problem.
Why Synthetic Data Is Different
Synthetic data is generated algorithmically, often using agents or simulators that mimic real user behavior. The key advantage is volume. A well-tuned agent can produce thousands of trajectories in an afternoon, each with structured labels and consistent formatting. This is not about replacing all real data, but about creating a high-quality baseline that captures the distribution of your task space. For example, a synthetic dataset covering 500k browser navigation tasks can cover edge cases, such as unusual error states or rare workflows, that real data rarely shows. The cost per sample drops from minutes to seconds, dramatically reducing the total engineering hours required.
Key Tradeoffs to Understand
- Realism vs. Coverage: Synthetic data often covers edge cases well but may not perfectly match the distribution of real interactions. Combining synthetic and real data mitigates this risk.
- Label Quality: Synthetic labels can be precise and consistent, but hallucinations or misaligned intents can creep in. Validation steps are necessary.
- Domain Specificity: Highly specialized domains (e.g., enterprise SaaS workflows) require careful design to ensure the generated behavior is realistic and useful.
- Compute vs. Headcount: Synthetic generation scales with compute, not people. You can batch generation and run it overnight, freeing engineers for other work.
The most effective approach starts with a small synthetic dataset to explore the design space, then iterates with real data to refine edge cases. This hybrid method delivers high coverage with a manageable engineering burden.
How Coasty Fits
Coasty runs computer-use agents on real desktops and browsers, capturing realistic interaction data that can be transformed into synthetic datasets and trajectories. The service is custom and contact-led, meaning you work with the team to define your specific task space, edge cases, and quality criteria. There is no fixed product or public pricing; instead, you discuss your needs and get a tailored approach. This lets you focus on your model and evaluation pipeline while Coasty handles the data generation and labeling.
Scaling synthetic data generation does not require hiring more engineers. You can reach high volumes and broad coverage by leveraging agents that generate realistic interaction data. If you want to explore how Coasty can help you build custom synthetic datasets for your AI agents, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .
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