AutoSynthData
ServiceNow CoreAI's pipeline that turns an enterprise agent's capability gaps into validated synthetic training tasks, improving the target model without touching customer records.
Why it matters
Closed-loop curriculum generation for enterprise agents: diagnose what the target model fails at, characterize solvable gaps with a stronger teacher, then generate, verify, and repair executable training tasks — 2,000 synthetic samples in ~18h yielded a 35% relative Pass@1 improvement on EnterpriseOps Gym Hybrid.
What you could build with it
An enterprise IT team or AI startup could adopt this failure-driven synthetic data recipe to train its own support-ticket or procurement agents: generate realistic workflow tasks from capability cards derived from failed runs, verify and repair each task automatically, and fine-tune without ever exposing production logs.
Does it hold up?
Too early to judge — published Oct 2 as a research blog post. Reported gains (+7.2pp Pass@1, closing 59% of the gap to the reference model on EnterpriseOps Gym Hybrid) are ServiceNow's own runs; the company notes it has not published head-to-head results against agents trained on real-data baselines.
Built with AutoSynthData
- Enterprise Agent Data Gap Targeted by ServiceNowfathom.news · News coverage of the Friday release, framing the enterprise agent training-data gap it targets.
- CV Brief · Saturday, 3 October 2026buttondown · Saturday-morning newsletter roundup including the AutoSynthData release.
- ServiceNow/EnterpriseOps-Gymgithub · The enterprise agent benchmark (1,150 expert-curated tasks, 8 domains) ServiceNow used to demonstrate AutoSynthData's gains.
Learn more
First spotted on hf: source.
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