Quality Gate for AI Systems

A control point that applies versioned criteria to decide whether a change may advance through the pipeline. The gate can combine deterministic regressions, probabilistic evals, security, cost, latency, and compliance. The evaluation policy defines metrics, thresholds, segments, and owners; the quality gate executes that policy and produces an auditable decision.

A passing average should not offset a critical regression in a protected segment or an irreversible operation. The gate consumes evaluation regression, but it may also require absolute limits. Mature gates distinguish automatic blocking, human review, and temporary exceptions, each with an owner, rationale, and expiration.