Synthetic Data for Fine-Tuning LLM Agents: What Actually Works
You cannot fine-tune a computer-use agent on nothing. You need examples that show realistic mouse movements, clicks, form fills, and reasoning across complex tasks. Real data solves this but comes with baggage: it costs money to collect, it carries privacy risks, and it is noisy. Synthetic data solves the noise problem but can introduce other risks if the generator is naive. The real question is not whether to use synthetic data, but how to build it correctly so it actually improves agent performance.
Why Real Agent Data Is Hard to Get
Collecting data from live agents exposes you to edge cases, security boundaries, and high labeling costs. A single real session can involve multiple tools, dozens of tool calls, and many retries. To label it properly you need experts to trace decisions, compare outcomes, and annotate successes and failures. That process is slow and expensive, so most teams either use tiny datasets or rely on heuristics that do not generalize well.
The Core Problem with Naive Synthetic Data
A naive generator can produce tool calls that look correct but fail when executed because it never sees the actual UI state, permissions, or backend responses. In tests, researchers have seen naive synthetic datasets produce up to 30% higher failure rates on edge cases compared to human-designed benchmarks, even though the synthetic samples look superficially plausible. The fix is not just more volume; it is higher fidelity and better coverage of the decision space.
Techniques That Actually Improve Agent Performance
- ●Use a grounded simulator that can render the same UI your agents will see, including real permissions and network errors.
- ●Run multiple agent variants on identical scenarios and keep only those that reach a verified success state.
- ●Inject adversarial cases such as permission errors, timeout states, and UI glitches so the model learns robust error handling.
- ●Combine synthetic trajectories with a small amount of real data to calibrate success criteria and reduce hallucinations.
- ●Validate each synthetic trajectory end-to-end before adding it to your training pool, ensuring tool calls map to real actions.
High-quality synthetic data is not about volume alone. It is about coverage, fidelity, and end-to-end verification so that every example you train on is actually achievable in the real world.
How Coasty Helps You Build Realistic Synthetic Datasets
Coasty runs computer-use agents on real desktop and browser environments, so it can capture realistic user interaction data that reflects actual UI states, permissions, and tool behaviors. That interaction data can be transformed into synthetic datasets and trajectories for training and evaluating your own agents. Because Coasty's work happens on real machines, the synthetic samples reflect actual system constraints and edge cases rather than idealized assumptions. This makes the resulting data more reliable for fine-tuning and benchmarking.
If you are building agents and hitting data bottlenecks, synthetic data can close the gap without exposing real users or inflating labeling costs. To explore a custom synthetic data solution for your workflows, book a data call with the Coasty data team at https://cal.com/coasty/coasty-data-call .