The Data Flywheel: Synthetic Data for Self-Improving Agents

Why agents need more than raw compute

Training a model that can use a computer is different from training a chatbot. You need sequences of actions, clicks, scrolls, form fills, navigation paths, not just text turns. These trajectories contain subtle cues: timing, layout changes, error states, and multi-step workflows. Real-world logs are noisy and fragmented. They often include human corrections, retries, or failed attempts that models struggle to learn from directly.

The cost of real interaction data

Let’s look at concrete numbers. A typical desktop automation task might require 50 to 200 mouse movements and keypresses per goal. To reach statistically robust coverage across 10 different domains (e.g., CRM, billing, analytics, support), you could need tens of thousands of unique trajectories. Collecting and cleaning that manually takes months. Automating data collection helps, but you still must label each sequence to annotate success, failure, and the intermediate steps that led to each outcome. High-quality labels for tens of thousands of trajectories can easily exceed $100,000 in manual effort, plus the ongoing operational cost of safe, controlled environments.

Synthetic trajectories close the loop

Synthetic data solves this by generating realistic interaction sequences in a safe, controlled environment. You define the goal and the constraints, then let an agent explore. The synthetic data is already labeled: every action, every intermediate state, and every outcome are recorded. This lets you scale experiments that would be impractical with real systems. For example, you can generate 100,000 unique workflows, test dozens of agent strategies, and compare performance on identical conditions. Synthetic trajectories let you fail fast, iterate quickly, and focus on the rare edge cases that real logs rarely expose. The result is a data flywheel: better models produce better synthetic data, which in turn trains even better models.

Key tradeoffs to keep in mind

• ●Realism vs. diversity: Synthetic data can be highly diverse, but it may not fully replicate the nuances of real user behavior, such as typos, hesitations, or unexpected layouts.
• ●Coverage vs. fidelity: You can cover more edge cases in a synthetic environment, but you must validate that the generated sequences are behaviorally plausible.
• ●Labeling complexity: Generating trajectories is straightforward, but ensuring accurate labels for every step and outcome requires careful design of success conditions.
• ●Integration overhead: Synthetic data must be integrated into your training and evaluation pipelines, which can add engineering effort depending on your infrastructure.

How Coasty fits the data flywheel

Coasty runs computer use agents on real desktops and browsers. This means the synthetic trajectories it generates reflect real interfaces, workflow patterns, and edge cases, not idealized simulations. Teams can work with the Coasty team to define specific goals, constraints, and domains, producing custom synthetic datasets and interaction trajectories tailored to their agents. Coasty’s approach is custom and contact-led, there is no self-service product or fixed package. The focus is on delivering high-quality, realistic data that you can directly plug into your training and evaluation pipelines.