Synthetic Data for Fine Tuning LLM Agents
Fine-tuning large language model agents works best when you have plenty of realistic interaction data. Real-world logs are expensive and often limited to a narrow set of tasks. You also face privacy risks and compliance constraints. Synthetic data solves these problems by generating high-fidelity interactions in controlled environments.
What makes agent training data different
Agent training requires more than just text prompts and model outputs. You need sequences of actions, tool calls, file operations, UI states, and failure modes. A single query can involve multiple steps: analyze a document, send an email, update a spreadsheet, or browse a web page. The sequence of these events determines how well an agent can generalize. Real-world logs rarely capture the full breadth of such workflows. Synthetic data can fill that gap.
Synthetic trajectories add coverage and safety
Synthetic trajectories let you create thousands of valid and invalid agent runs in controlled environments. You can program edge cases, permission errors, unexpected inputs, and retry strategies. Research shows that synthetic data can reduce the gap between training and evaluation performance when mixed with real logs. For example, a 2023 study found that adding synthetic trajectories to a small real dataset improved out-of-distribution agent accuracy by 12, 18 percent. Synthetic data also gives you control over privacy-sensitive information, so you can avoid regulatory headaches.
Techniques for high-quality synthetic agent data
- ●Replay real user sessions as trajectories by logging tool use, UI state changes, and intermediate states.
- ●Use controlled environments to generate diverse workflows across different applications and workflows.
- ●Inject intentional errors and permission constraints to teach agents how to handle failures gracefully.
- ●Filter synthetic interactions for relevance, clarity, and consistency before fine-tuning.
The key takeaway: synthetic data enables you to train agents on realistic, diverse, and private workflows without relying on scarce real logs.
How Coasty fits
Coasty runs computer use agents on real desktops and browsers to capture realistic interaction data. This lets you generate synthetic datasets and trajectories that reflect how agents actually operate in complex environments. Coasty’s service is custom and contact-led, meaning you work directly with the team to define your data needs and workflows. There is no fixed package or public price list. The focus is on delivering high-quality, task-specific synthetic data that fits your evaluation and training goals.
If you need synthetic data for fine-tuning LLM agents, book a data call with the Coasty data team to discuss your requirements and explore what’s possible.