Deployment Confidence in Era of AI Coding (techblog.cloudkitchens.com)

CloudKitchens reports practical gains in deployment reliability as code generation grows: by building an in-house canary system and standardized observability, they run canary on >80% of service releases (95% for critical order-fulfillment paths) and in Q3 2025 blocked over 1,100 bad releases that might otherwise have caused user-facing regressions or outages. The story underscores a pressing trend for AI/ML teams — as more code is produced by generative models, lightweight, automated guardrails (not just heavy manual review) are essential to keep regressions from reaching customers. Technically, they automated dashboard/alert creation by scanning Kubernetes services and mapping Prometheus metrics into Grafana, then partition services into baseline, canary and main groups so metrics from canary vs baseline can be compared during rollout. Early statistical gating used the Mann–Whitney U test, later augmented with a Proportional Check (Fisher’s exact test) that performs better on small-sample proportions. They tightened granularity by integrating canary weighting with Istio, added end‑to‑end trace canaries to catch indirect failures (schema or endpoint deletions), and instrumented true/false positive/negative rates — using LLM-backed labeling to speed validation. The practical takeaway for ML engineering: combine metric-standardization, service-mesh traffic control, sensible statistical tests, and automated labeling to keep AI-written code safe in production.