Synthetic Data vs Real Data for Training AI Agents: What You Actually Get

The problem with real interaction data

Building a computer-use agent means capturing the messy reality of clicking, typing, and navigating across apps, settings, and edge cases. In practice, you might only have a few hundred hours of recorded sessions for a niche workflow. That’s not enough to train a robust policy or evaluate edge behaviors reliably. Cleaning and labeling those logs adds more effort: you have to resolve naming conflicts, map internal IDs to user-facing terms, and tag intent for each click sequence. Real-world data also carries privacy risk. Financial services, healthcare, and regulated industries often cannot ship raw session recordings to a third‑party model provider, even if the data looks anonymized.

How synthetic data closes the gap

Synthetic data lets you generate thousands of trajectories that mimic the shape of real sessions. You define the workflow, the tools, and the possible user intents. A system then produces sequences of clicks, keystrokes, and UI states. The key benefit is controllability. You can systematically cover rare cases, edge states, error flows, and multi-step workflows, that never appear in production logs. One enterprise customer built a data set of 50,000 synthetic purchase journeys by specifying a catalog of products, promotion rules, and payment flows. They trained a planning agent that handled complex multi-step orders 20% faster on validation data than a model trained only on real logs. Another team generated 10,000 customer support tickets with varied intents and resolutions, then fine‑tuned a language model to prioritize tickets more accurately. Synthetic data can also be cheaper per sample than manual labeling, especially when you need millions of examples.

Real tradeoffs you should expect

• Realism gap: Synthetic trajectories often lack the subtle variations of human behavior, hesitations, typos, context switches, or environment noise.
• Coverage vs fidelity: You can easily cover all logical paths, but you may miss the unpredictable patterns that only emerge in messy real usage.
• Evaluation drift: Models evaluated on synthetic data can overfit to the simulation and underperform when deployed on real interfaces.
• Domain knowledge cost: You must explicitly encode your domain logic to generate believable scenarios, which is more upfront work than just dumping logs into a model.

How Coasty fits into the mix

Coasty runs computer-use agents on real desktops and browsers, which lets it capture realistic interaction data for a wide range of environments. That same capability can be directed to generate custom synthetic datasets and trajectories tailored to your workflows and guardrails. The offering is custom and contact-led, you work with the Coasty team to define objectives, scopes, and constraints. There’s no fixed package or public price list. The goal is to produce datasets that reflect the complexity of your actual workflows while staying within your compliance and privacy boundaries.