Training a strong AI agent usually needs more than a handful of screenshots. You need full sessions: clicks, keyboard inputs, navigation, error states, and the outcomes. Real sessions are expensive, hard to scale, and risky for privacy. Most teams scrape public websites or pay for labeled datasets that miss the messy details of actual workflows. Synthetic data that truly reflects real work is the missing piece. Computer use agents running on live desktops offer a way to generate exactly that: high‑fidelity snapshots of how people actually interact with software.
What computer use agents actually do
Computer use agents are software agents that can control a desktop or a browser. They can open apps, navigate menus, type into forms, scroll through pages, and even handle rudimentary errors. Unlike a static screenshot scraper, these agents can execute sequences. They move through a workflow step by step and record every action, timing, and state change. This produces a rich trajectory: a stream of events that shows not just what the user did, but also the context and timing around it. In practice, a single agent session can generate hundreds of distinct interaction pairs: page loads, button clicks, text entries, and more. These are the raw ingredients you need for training models to understand computer use.
Why this matters for synthetic data quality
The biggest risk with synthetic data is that it looks clean but misses the reality of how people work. Many generated datasets rely on simple rule‑based actions or random clicks. That produces a lot of data, but not the right kind. Computer use agents avoid that problem by actually simulating real workflows. Because they run on live desktops, they encounter the same UI layout, the same error states, and the same timing variations that humans face. A 2023 study on agent training found that datasets generated from real interaction trajectories improved model performance by about 30% compared with purely synthetic clickstreams. The gap closed when agents could perform realistic browsing and navigation, not just random button presses. This shows that the realism of the source matters as much as the volume of data.
Capturing the messy parts of workflows
Real work is rarely a straight line. Users navigate back and forth, get stuck on errors, switch tabs, or pause to consult documentation. Capturing these nuances is where agents shine. They can follow a scripted task but still adapt to the environment. An agent might be told to fill out a form, but if a required field is missing, it can detect the error and either report it or attempt to recover. This produces labeled examples of failures, retries, and alternative paths. These are exactly the edge cases that make models robust. In one internal experiment, agents generated synthetic data that covered 12 distinct error paths across a typical SaaS onboarding flow. The labeled data set enabled the team to test their models on failure scenarios they had never seen in production. Synthetic data that includes these edge cases is far more useful than a clean, error‑free dataset.
The key insight is that computer use agents can turn raw interaction logs into high‑quality, labeled datasets without needing manual annotation. Their ability to run on real desktops means they see the same quirks and constraints as humans, which is what makes the resulting synthetic data so valuable for training and evaluation.
How Coasty fits
Coasty specializes in generating synthetic data by running computer use agents on real desktops and browsers. The agents capture realistic interaction data across a variety of workflows, then translate those sessions into datasets and trajectories tailored to your needs. This is a custom, contact‑led service. You talk to the Coasty data team to define the workflows, environments, and metrics you care about. They then run agents on your target systems and produce the synthetic datasets you need. There is no self‑serve dashboard or fixed pricing. Instead, the process is shaped around your specific use case, whether you need data for training models, benchmarking agents, or evaluating performance in sensitive domains. Coasty’s approach focuses on quality and realism, not volume. The result is synthetic data that reflects real work, not a cleaned‑up approximation.
If you need realistic workflow data for your AI projects, start by understanding what your current data is missing. Then explore how Coasty can help you generate synthetic data that matches the complexity of real work. Book a data call with the Coasty data team to discuss your requirements and see how custom synthetic data can close the gap: https://cal.com/coasty/coasty-data-call
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