Model drift silently erodes AI reliability: how to spot it early
AI model drift occurs when production data, user behavior, prompts or business rules diverge from training assumptions, causing gradual quality loss even as uptime stays high. Honeycomb outlines concrete signals such as data and concept drift, pipeline failures, and prompt or embedding changes, and shows how observability tooling can surface these issues before they hurt users or revenue.