Synthetic Data vs Real Data for Training AI Agents

The real cost of real data

Gathering labeled interaction data at scale is expensive. A single, high-quality test case can take days to plan, execute, and label. Teams often end up with a few thousand examples and spend more on manual annotation than on model training. This is why data scarcity remains a blocker for deploying agents in production.

Benchmark: synthetic vs real for navigation tasks

A recent benchmark on web navigation showed that synthetic trajectories improved agent performance by 12, 18% when the synthetic dataset covered at least 1,000 diverse steps. The key was diversity: varied devices, browser versions, and error scenarios. Purely synthetic data that mimicked a single user pattern did not consistently close the gap. The takeaway: diversity and coverage matter more than raw volume.

Where synthetic data helps most

• ●Edge cases and rare errors that rarely appear in production logs.
• ●High-risk workflows (e.g., financial transfers, data deletion) without exposing real user data.
• ●Cross-platform and cross-device simulation for agents that must handle multiple environments.
• ●Rapid iteration cycles: test ideas with synthetic data before committing to costly real experiments.

How Coasty fits

Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This allows the team to generate synthetic datasets that reflect actual user behavior, including edge cases and multi-step workflows. The service is custom and contact-led: you work with the Coasty data team to define your requirements, and they produce synthetic trajectories tailored to your use case.