EVALUATION & RELEASE
How shadow and canary release apply to AI behavior
Why AI behavior changes benefit from a release path that separates offline evaluation, shadow evidence, canary health, explicit promotion, and rollback.
DefinitionA shadow release observes a candidate without serving its output as the authoritative response. A canary release serves the governed candidate to a bounded share or scope so online evidence can be collected before explicit full promotion.
KEY TAKEAWAYS
- Shadow execution observes a candidate without making its output authoritative.
- Canary release serves a bounded production scope and gathers online evidence.
- Promotion and rollback remain explicit release decisions rather than automatic health outcomes.
Offline evaluation and online evidence answer different questions
Offline evaluation asks whether a candidate behaves acceptably on known cases. Online release evidence asks whether that exact candidate remains healthy under real traffic, application state, providers, and operational conditions.
A mature release path keeps those authorities separate. Evaluation can make a revision eligible for release preparation without silently making it active.
Shadow first when served output must not change
Shadow execution is useful when teams want candidate observations without replacing the response users receive. The candidate runs out of band and produces comparison evidence.
Because shadow output is not authoritative traffic, the system can inspect regressions or provider failures while the baseline continues serving users.
Canary is a bounded production decision
Canary begins only after the release authority permits it. The target may be a traffic percentage or another explicit scope. Health evidence should remain tied to the same candidate revision, artifact, application, environment, and model target.
Passing canary health still should not be interpreted as automatic permission to promote. Promotion is a separate governed action.
SOURCE & LIMITATION NOTE
Product and Docs links support Vira architecture descriptions. External references are labeled separately. None of them should be read as benchmark evidence unless a dated methodology and result set is published.
REFERENCES