Better AI agents need better data, and better data lives in real workflows. But collecting that data is hard. You can't just scrape public websites. You can't safely record sensitive enterprise tools. And you often don't have the budget to manually design thousands of realistic scenarios. Synthetic data solves this gap by generating interaction trajectories that look and feel like the real thing. To get that data, teams are turning to computer use agents that run on live desktops and browsers, not in a closed simulator.
The data gap is real
Most industry benchmarks rely on small, curated environments like WebVoyager or WebArena. Those tasks are useful for research, but they don't cover the messy, multi‑step workflows that enterprises actually run. When you try to train an agent on production‑like tasks, you quickly hit three constraints: (1) Scale, you need thousands of distinct workflows to generalize, not a handful of demos; (2) Sensitivity, you can't expose real credentials, proprietary dashboards, or regulated workflows to training data pipelines; (3) Diversity, production data is biased toward what teams already do, not the edge cases you need to handle. Synthetic data lets you fill those gaps at volume without exposing real systems.
What computer use agents actually do
Computer use agents are autonomous programs that can control a desktop or browser like a human. They read screen content, interpret UI elements, plan multi‑step actions, and execute them in real time. The key difference from traditional bots is that they operate on the live OS or browser, not on a scripted mockup. This gives them access to real DOM structures, native controls, and system feedback. For example, an agent might navigate a CRM dashboard, search for open tickets, filter by status, and update a few fields based on business rules. Every click, keystroke, and scroll is recorded, and the session can be replayed as a training example.
Capturing real workflows at scale
Because agents run continuously, they can generate thousands of hours of interaction data in days. A recent internal study showed that a fleet of agents running 24/7 on a standard cluster can produce 10,000+ distinct workflow sessions in a single week. Each session is a complete trajectory: start state, intermediate actions, and final outcome. Some workflows are short, logging into a portal and submitting a form. Others span minutes: onboarding a new user, reconciling an invoice, or investigating a system error. The data includes screen captures, raw input streams, and semantic annotations, giving you a rich training set that mirrors real usage patterns.
Why live control beats closed simulators
Simulators are great for controlled experiments, but they have blind spots. They often model only a subset of UI elements, ignore system latency, and miss edge cases like unexpected error states or third‑party dialogs. Computer use agents operating on live systems see exactly what a human sees. They encounter the same UI quirks, same network delays, and same browser behavior. This makes the synthetic data far more realistic for evaluating agents that must work in production environments. Agents trained on simulator data can fail on simple real‑world nuances; agents trained on live‑control data are more robust out of the box.
The takeaway: Real workflow data is hard to gather safely and cheaply. Computer use agents bring that data to you by running on live desktops and browsers, generating high‑fidelity synthetic trajectories at scale.
How Coasty fits
Coasty specializes in generating synthetic datasets from real interaction data. By deploying computer use agents on clients’ actual desktops and browsers, Coasty captures genuine workflows and produces custom synthetic datasets and trajectories tailored to your use cases. The service is fully custom and contact‑led: there is no self‑serve portal and no fixed pricing. You talk to the Coasty data team about your requirements, and they design a solution that produces the right kind of data for your models.
If you need synthetic data that looks and behaves like real workflows, the best next step is to book a data call with the Coasty team. Visit https://cal.com/coasty/coasty-data-call to schedule a conversation about your data needs and how Coasty can help you build better training and evaluation sets.
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