How to Canary and Roll Back Model, Prompt, or Tool Changes Without Breaking Production

This article provides a comprehensive runbook for platform and AI engineers on how to canary and roll back changes to AI models, prompts, and tools without breaking production. It emphasizes treating every production AI change as a versioned behavior release, packaging model identifier, prompt, tool schemas, retrieval settings, policy, runtime code, and evaluation thresholds into one immutable bundle. The process includes offline validation, shadow mode traffic replay, sticky cohort canary testing, trace comparison, and automatic rollback when safety, correctness, reliability, or cost thresholds are breached. It highlights the danger of partial rollbacks and the importance of reverting the complete bundle to avoid hidden failures.