Key Takeaway
By the end of this blueprint you will have an AI-aware feature flag system with percentage rollouts, quality-gated promotion that automatically advances rollout when evaluation scores are stable, and kill switches that instantly disable degraded AI features without a code deployment.
Prerequisites
- An existing feature flag service (LaunchDarkly, Unleash, or custom) or willingness to build one
- An AI observability stack producing quality scores (see AI Observability Stack blueprint)
- Python 3.11+ or TypeScript 5+ for the SDK
- Redis for flag state caching and quality signal aggregation
Why Standard Feature Flags Are Not Enough
Standard feature flags gate features on error rates and user targeting. AI features fail differently — a model upgrade can return valid HTTP responses with structurally correct output that is subtly wrong, off-brand, or unsafe. Error rate stays at zero while quality drops. You need flags that understand quality metrics, can gate on evaluation scores, and automatically respond to quality degradation without human intervention. This is the gap between a regular feature flag and an AI feature flag.
Architecture Overview
The system extends a standard feature flag service with AI-specific evaluation hooks. When a flag is evaluated, the SDK checks both the targeting rules and a quality signal aggregator that pulls recent evaluation scores from the observability stack. A promotion controller gradually increases rollout percentages when quality metrics remain stable and triggers automatic rollback when they degrade beyond configurable thresholds.
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