Most teams hit the same wall: they need realistic, labeled data to train or evaluate AI agents, but real workflow data is hard to get. It can be expensive, slow to collect, and risky to share. Synthetic data offers a way out, but you need a source that can actually mimic real user actions. Computer use agents are one of the most promising sources.
Why synthetic data matters now
Models need millions of examples to generalize, and agents need trajectories that show how decisions flow into actions. Real datasets for complex workflows are rare. A Fortune 500 survey found 62 percent of AI projects stall because they lack sufficient labeled data. Synthetic data lets you generate that volume on demand, without the privacy, security, or logistical headaches of collecting real interactions.
How computer use agents generate workflow data
Computer use agents live on real desktops and browsers. They don’t just predict clicks; they perform tasks end-to-end, open apps, navigate menus, fill forms, handle errors, and save or export results. Every mouse movement, keystroke, and screen change gets logged. This creates a detailed trajectory that reflects how a human would actually interact, not just the final outcome. Because the agent operates in a live environment, it encounters real UI elements, dynamic states, and side effects, giving the synthetic data a layer of realism that scripted simulations lack.
Key data types captured by agents
- Full session logs: timestamps, UI states, action sequences, and outcomes.
- Interaction metadata: window focus, scroll depth, field validation messages, and error states.
- Contextual signals: application states, logged-in sessions, and transient UI elements.
- Side-effect traces: file saves, network calls, database writes, and process exits.
The most valuable synthetic datasets combine high-fidelity interaction logs with ground-truth labels that describe intent, decision points, and successful task completion.
What you gain compared to real data
- Speed: generate weeks of workflow data in hours instead of months.
- Safety: no need to expose proprietary workflows or customer data.
- Variety: replay workflows under different settings, error conditions, or UI changes.
- Cost predictability: no per-record fees or ad hoc data collection projects.
Synthetic data is not a drop-in replacement for all use cases, but for agent training and evaluation it provides a scalable, controllable foundation.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This lets you generate synthetic datasets and trajectories tailored to your workflows, whether you need customer support simulations, internal tool automation sequences, or domain-specific tasks. Coasty’s service is custom and contact-led, you work directly with the team to define requirements, scope, and quality standards. There is no self-serve portal and no fixed packages; everything is scoped around your use case.
If you need realistic workflow data without the risks or delays of collecting real interactions, synthetic data is the practical path forward. To explore what’s possible for your project, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call.
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