AI agents that interact with software and users need a lot of data to get good. Real data is often limited, expensive to label, or hard to obtain because of privacy, security, or business constraints. Synthetic data offers a way around these limits, letting teams generate high‑quality trajectories and samples at scale. This creates a data flywheel: better agents produce better data, which makes the agents even better.
The real data bottleneck
Training agents that use tools, navigate UIs, and reason through multi‑step tasks requires large amounts of interaction data. Real‑world data has three main problems. First, there is scarcity: each real session is unique and cannot be repeated. Second, there is labeling cost: annotating every click, error, and outcome is time‑consuming. Third, there is risk: exposing sensitive user data or proprietary workflows can lead to compliance issues. Teams often rely on a few hundred or a few thousand labeled examples, but modern agent benchmarks show that performance drops sharply beyond that range.
What synthetic data actually buys you
Synthetic data is artificially generated data that mimics real interactions with enough fidelity to train or evaluate models. One key benefit is control: you can generate specific edge cases, rare error paths, or multi‑step workflows that are hard to capture in production. A recent study on automated software agents showed that adding synthetic trajectories improved downstream performance by 15, 25 percent compared to using only real data. Another experiment in customer support chatbots demonstrated that synthetic examples of obscure intents raised intent accuracy by 12 points after just a few hundred iterations. These gains come from exposing the model to more diverse scenarios without the cost of labeling them. Synthetic samples also let you enforce constraints on data quality, such as removing hallucinations or ensuring safe tool usage, which is harder to enforce on raw logs.
Techniques that actually work
- Trajectory replay and replayable environments: simulate realistic sequences of actions, capture intermediate states, and generate new variations by perturbing parameters.
- Code‑driven generation: use existing workflows or scripts to generate structured inputs and expected outputs, then add noise or alternative paths.
- Hybrid labeling: start with a small set of real examples, then use models to predict labels for synthetic samples and refine iteratively.
- Quantitative evaluation: use synthetic benchmarks to measure improvements in success rates, latency, and robustness before moving data into production.
The most effective synthetic data pipelines treat generation and evaluation as a closed loop: agents train on synthetic data, their outputs are measured, and the best performing examples are fed back into the generation process. This feedback loop is the core of the data flywheel.
How Coasty fits into the flywheel
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This means synthetic datasets and trajectories are grounded in how humans actually operate software, rather than relying solely on simplified simulations. Coasty offers a custom synthetic data service designed around your specific agent use cases. This is a contact‑led engagement: you discuss requirements, define scenarios, and work together to build targeted datasets and evaluation benchmarks. There is no self‑serve product or fixed price list. The goal is to produce high‑quality synthetic data that accelerates your own data flywheel, whether you are training new agents or evaluating existing ones.
Building a data flywheel starts with asking what your agents still need to learn. Synthetic data can close those gaps faster and more safely than waiting for real-world events. If you want to explore how Coasty can help generate custom synthetic datasets for your agents, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .
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