The production mindset is simple: every material change to prompts, models, tools, or routing logic should go through a measurable release decision, not a hope-driven rollout.
The fastest way to lose trust in an AI workflow is to let behavior drift invisibly after launch. Evaluation harnesses prevent that by making performance, safety, and review quality observable both before and after changes ship.
Run golden-set tests, prohibited-action checks, and threshold validations against the exact workflow slice you plan to change.
Track drift, reviewer overrides, escalations, latency, and cost envelopes on a fixed cadence instead of waiting for complaints.
Promote novel failures and reviewer notes into the test corpus so the harness improves with the workflow.