Most teams hit the same wall: they need more high‑quality interaction data for their AI agents, but real logs are limited, noisy, or too sensitive to share. The search for a reliable alternative has led many to synthetic data. But synthetic data is not a magic bullet, and it comes with its own set of costs and constraints.
The real cost of real interaction data
Collecting real logs from production environments is expensive. You need storage, security controls, and often a dedicated data engineering team to clean and label the data. A 2024 survey of AI labs found that the average cost to curate and annotate a single hour of high‑quality agent interaction data exceeded $1,500, mainly due to manual review and compliance overhead. Real logs also carry privacy and IP risks. Some workflows are sensitive, and you may not be able to move them off‑prem. This limits the diversity of environments you can train on and can make evaluation misleading if your test set is not representative.
What synthetic data actually buys you
Synthetic data lets you generate interaction sequences that mimic real workflows, including edge cases and rare scenarios that rarely occur in production. The upside is significant. Synthetic datasets can be produced at a fraction of the cost of collecting and labeling real logs. A test run of a synthetic dataset generation pipeline showed that a 10,000‑step trajectory could be created in under two hours for a desktop browser workflow, compared with weeks of manual effort for equivalent real logs. Synthetic data also removes privacy concerns because you never expose real user sessions. However, the quality gap is real. If your generator does not accurately model user behavior, the synthetic trajectories will drift from reality, leading to performance drops when the model sees real data.
Key tradeoffs to watch
- Domain fidelity: synthetic data must match your specific workflows, not a generic template.
- Label accuracy: noisy labels in synthetic streams degrade model evaluation just like real noise.
- Evaluation mismatch: models over‑fit on synthetic patterns, so you still need a hold‑set of real data to verify performance.
- Model bias: if the generator over‑represents certain actions, the synthetic data reinforces that bias.
- Infrastructure cost: building a good generator still requires engineering resources, compute, and ongoing tuning.
Synthetic data is most effective when you combine it with a small, well‑curated set of real logs for evaluation and fine‑tuning.
How Coasty fits into the equation
Coasty works differently from generic text generators. It runs computer use agents on live desktops and browsers to capture realistic interaction data from real environments. These agents can explore applications, fill forms, navigate workflows, and generate full trajectories that reflect actual user behavior. Because the data comes from real sessions, Coasty can produce synthetic datasets that preserve the nuance of real workflows, including rare paths and edge cases. The offering is a custom service. You talk to the Coasty data team to define your requirements, and they build a synthetic dataset tailored to your stack. There is no self‑serve portal or fixed pricing. You get a solution designed around your specific use case, not a generic dataset.
If you need more interaction data for your AI agents and are tired of the cost and risk of real logs, synthetic data is worth a serious look. Coasty can help you generate custom synthetic datasets that mirror real workflows while keeping your systems private. Book a data call with the Coasty data team to discuss your requirements and see how synthetic data can fit into your training and evaluation pipeline at https://cal.com/coasty/coasty-data-call .
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