The Enterprise AI Behavior Control Plane: Governing Longitudinal Drift in Production LLM Systems

Abstract

The rapid integration of Generative AI into enterprise-critical workflows has introduced a novel systems challenge: behavioral instability over time. Unlike deterministic software, Large Language Models (LLMs) exhibit probabilistic outputs that shift due to model updates, prompt changes, retrieval corpus evolution, and shifting user intent distributions. This paper introduces the Enterprise AI Behavior Control Plane (BCP)—a dedicated platform-layer architecture designed to measure, benchmark, detect, and constrain behavioral drift in production GenAI systems. Drawing on Site Reliability Engineering principles, the BCP establishes behavioral baselines, enforces AI-specific service level agreements, continuously monitors longitudinal drift, and enables automated rollback strategies. Through empirical analysis across production deployments, we demonstrate that behavioral drift affects 23.9% to 62.0% of deployed agents over extended interaction sequences, with task success rates degrading by up to 42.0% in uncontrolled environments. Our proposed framework provides the first systematic architecture for treating enterprise AI governance as a continuous control problem rather than a static evaluation task.