Self-Regulating AI Agents: A Runtime Constitutional Framework for Autonomous Decision Systems in Cloud-Native Environments

Abstract

The increasing trend of deploying autonomous AI agents in cloud-native environments has enabled the automation of real-time and large-scale decision processes in enterprise and industrial systems. Nevertheless, the existing governance and alignment mechanisms remain external to the operational cycle of the agents. The current mechanisms rely on static policies and offline validation or infrastructure-level controls. Such mechanisms are not effective for agents that dynamically plan and collaborate and change their behavior at runtime. This paper proposes a runtime constitutional framework for self-regulating autonomous decision systems deployed in cloud-native environments. The framework incorporates machine-understandable governing principles into the operational cycle of AI agents and enables the monitoring, contextualization, and correction of every decision made by the agents before their execution. The framework is designed as an architectural structure comprising a constitutional rule layer, contextual state observation, decision interception, constitutional reasoning, and self-correction and adaptation. This makes the framework an independent mechanism of governance. Unlike existing alignment and policy enforcement mechanisms, the framework focuses on the regulation of the behavior of the agents at runtime and not at the training or deployment stages. This paper proposes an architectural and methodological contribution that offers a scalable and platform-independent solution for the runtime governance of autonomous decision systems. The proposed framework supports the development of dynamic cloud infrastructures and multi-agent systems, offering a practical solution for the development of reliable and human-aligned autonomous decision systems.