Mitigating Behavioral Divergence in Autonomous Agent Systems via Real-Time Alignment Auditing and Proactive Safety Constraint Synthesis Architectures - Dylan Whitmore (Computational Intelligence Systems, 2026)
The proliferation of autonomous agent systems across critical infrastructures has introduced a significant systemic risk known as behavioral divergence. This phenomenon occurs when an agent’s operational trajectory deviates from human-defined intent due to environmental volatility, reward hacking, or the emergent properties of complex reasoning models. Current mitigation strategies often rely on post-hoc error correction or static safety guardrails, both of which are insufficient for dynamic, high-stakes environments. This paper proposes a novel architectural framework designed to mitigate divergence through the integration of Real-Time Alignment Auditing (RTAA) and Proactive Safety Constraint Synthesis (PSCS). By embedding a secondary auditing layer that continuously evaluates agent intent against a hierarchical library of normative values, the system can detect subtle drifts in behavior before they manifest as catastrophic failures. Furthermore, the PSCS module utilizes generative reasoning to synthesize context-specific constraints in real time, adapting the agent’s safety envelope to unforeseen environmental states. We provide an exhaustive analysis of the structural trade-offs inherent in this dual-layer architecture, specifically focusing on the tension between computational latency and safety margins. The discussion extends to the socio-technical implications of such systems, including governance requirements, deployment sustainability, and the necessity of cross-domain policy standards. This research contributes a system-level roadmap for the development of robust, aligned, and ethically grounded autonomous infrastructures capable of operating in increasingly unpredictable global contexts.