Self-Regulating Artificial Intelligence: Architectural Governance for Long-Lived AI Systems

The operational landscape of Artificial Intelligence (AI) is shifting from isolated, static deployments toward dynamic, long-lived ecosystems. As autonomous systems, such as digital twins and adaptive healthcare monitors, function over extended timelines, the traditional "train-validate-deploy" lifecycle is becoming inadequate. These "Long-Lived AI Systems" (LLAIS) encounter severe stability risks, most notably concept drift, catastrophic forgetting, and the gradual erosion of safety alignment. Furthermore, conventional governance frameworks often rely on periodic external audits. Consequently, they lack the temporal resolution required to intercept real-time failures in autonomous agents. To address this gap, we propose the Self-Regulating Governance Architecture (SRGA), a novel framework designed to internalize oversight. The SRGA embeds a deterministic "Governance Controller" alongside the stochastic learning module, employing Signal Temporal Logic (STL) constraints and conformal uncertainty quantification. This architecture empowers the system to autonomously detect distribution shifts and execute immediate interventions, specifically Decision Freezing and Version Rollback. We evaluate the framework via rigorous simulation on the CIFAR-10-C benchmark. Results indicate that SRGA improves the Safe Failure Rate from 12.0% to 99.5% compared to standard continual learning baselines while preserving predictive utility. We argue that internal self-regulation is a prerequisite for the ethical deployment of autonomous systems in high-stakes environments.