Training and evaluating AI agents that interact with software is hard. Real work is messy: menus change, workflows vary, and success often depends on subtle context. Most teams rely on logged user activity. That data is noisy and incomplete. It also carries privacy risks and can’t be easily scaled. Synthetic data offers a clean alternative, but it must mirror reality to be useful. Computer use agents change the game by acting on real systems, not simulations.
Why workflow data matters more than screenshots
Screenshots alone don’t explain what an agent does or why it fails. You need sequences of actions: clicks, keystrokes, mouse movements, and prompts across tools. A real agent going through a workflow leaves behind a complete trajectory of interactions. That trajectory shows how tasks decompose, where dependencies exist, and where agents can get stuck. One study of 150 enterprise workflows found that 60% of failure modes were caused by missing context rather than poor model performance. That context lives in the sequence of actions, not in a static screenshot.
Computer use agents run on real systems
Computer use agents interact directly with applications and browsers. They don’t just reason about a UI; they click, type, and navigate. This physical interaction means every step is grounded in a real environment. When an agent opens a browser, fills a form, and uploads a file, each action is executed on an actual machine. This realism is unique. Many synthetic data approaches rely on mockups or simplified environments that miss real-world quirks. Agents that act on real systems capture those quirks automatically. They expose edge cases, version mismatches, and permission issues that simulations often hide.
Capturing realistic error and retry patterns
Real workflows are filled with retries and error handling. An agent might get a CAPTCHA, encounter a timeout, or need a manual intervention. Capturing these moments is crucial for training robust systems. A benchmark on web agents showed that models trained on synthetic data with simulated errors improved by 18% on unseen error types. That boost came from including real-world failure patterns rather than perfect, error-free trajectories. Computer use agents naturally generate these patterns because they handle actual system responses. They can retry tasks, switch tools, and recover from real failures, creating richer datasets without manual scripting.
Scaling workflows without scope creep
Building labeled synthetic data manually is expensive and slow. You have to define scripts, define expected outcomes, and manually verify results. Computer use agents automate this process. Once configured, they can run hundreds of workflows on different machines, each with unique inputs. They log every interaction and can even generate ground-truth labels from system events. This automation lets teams generate large synthetic datasets quickly. One engineering team reported a 5x reduction in labeling time after deploying agents to generate synthetic support ticket workflows. The agents handled routing, categorization, and response drafting, creating labeled examples that reflected real-world variability.
Synthetic data only works if it reflects reality. Computer use agents execute actions on real systems, capturing messy, realistic workflows and error patterns that static data or mockups miss.
How Coasty fits
Coasty runs computer use agents directly on real desktops and browsers. This setup lets teams capture genuine interaction data from enterprise workflows and then turn that data into custom synthetic datasets. The Coasty approach is custom and contact-led. Teams specify the workflows, tools, and success criteria, and Coasty generates synthetic data that mirrors those realities. No fixed packages or public pricing exist. Instead, every engagement is tailored to the project’s scope and data needs.
If you need synthetic data that truly reflects real workflows, talk to the Coasty data team. Book a data call to explore how computer use agents can generate high-quality, representative datasets for your AI agents and models: https://cal.com/coasty/coasty-data-call
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