Release Engineering for AI Features: Rollouts, Rollbacks, and Runtime Risk
Shipping ordinary software rests on a useful assumption: the artifact you test is mostly the artifact your users receive. A container image can be pinned, a database migration can be reviewed, and a feature flag can be switched off when a release goes wrong. AI features weaken that assumption because application behavior can change even when the application code has not changed.
A model provider can update a model, a prompt can be edited, a retrieval index can be refreshed, a tool schema can change, or a safety rule can alter the path a request takes. Each of those changes can shift output quality without producing a conventional deployment event. Release engineering for AI therefore has to manage not only code, but also behavior.
The Release Artifact Is No Longer Just Code
In a conventional service, a release can often be described with a commit hash, build artifact, configuration version,...
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