AutoSynthData: Generating Training Data for Enterprise Agents

This post introduces AutoSynthData, a system for generating synthetic training data for enterprise agents. It details the process of identifying agent capability gaps through model failures and teacher successes, then generating and validating new tasks. The system uses a `(system specification, user prompt, verifier)` abstraction for tasks and emphasizes feasibility, realism, and difficulty for training signal. The implementation is illustrated with EnterpriseOps Gym experiments, including hybrid and ITSM scenarios, and highlights the importance of sample-level verification and batch-level review for high-quality synthetic data.