AI systems change more often than traditional software. Providers update models, teams tweak prompts, knowledge bases ingest new content, and tools evolve. Without controlled rollout patterns, small changes create unpredictable regressions.
Canary rollouts introduce changes to a small slice of traffic, measure outcomes, and expand only when thresholds are met.
Define success thresholds
Include both quality and risk signals: escalation/refusal rates, tool error rates, latency SLOs, and groundedness indicators.
Pair canaries with rollback levers and incident response so failures are contained (see incident response).