Sequential Hypothesis Testing for Safe Production Migration of Non-Deterministic Analytical Agents in Latency-Critical Serving Infrastructure

Abstract

The deployment of updated large language model-based analytical agents in production environments presents a statistically and operationally complex challenge that conventional software deployment strategies are fundamentally ill-equipped to address. Unlike deterministic systems, where a single test request can expose a defect, stochastic analytical agents require statistical aggregation over many observations to detect quality differences between versions. Existing deployment practices, including canary releases, blue-green deployments, and fixed-window A/B testing, each suffer from distinct failure modes: population confounding, binary risk exposure, or temporal inefficiency. This article proposes a deployment framework that adapts dual-write and dual-read consistency patterns from distributed database migration to the domain of non-deterministic agent serving, integrating them with Wald's Sequential Probability Ratio Test to provide formal statistical guarantees on migration decisions. The framework introduces a Confidence-Gated Migration Protocol that evaluates agent quality across four dimensions, analytical correctness, latency profile, semantic equivalence, and safety, using parallel sequential tests with familywise error control. Phased migration through shadow evaluation and canary deployment, governed by site reliability engineering error budget principles, constrains quality assessment to formal error bounds across all evaluation phases. Simulation results indicate a ninety-six percent rate of detecting regressions, a forty-one percent reduction in mean time to safe migration, and a false alarm rate of less than four percent.