Runtime Oversight of Autonomous Agents

As autonomous artificial intelligence systems become increasingly capable and ubiquitous, the limitations of static governance methods have become more pronounced. This paper proposes a conceptual architecture for runtime oversight of autonomous AI agents, introducing the notion of behavioral scoring models that operate during deployment to enhance safety, compliance, and accountability. We review existing literature on runtime verification, auditability in complex systems, and safety monitoring in AI; we then outline a structured architecture for behavioral oversight that combines continuous telemetry, dynamic evaluation, policy enforcement, and audit evidence capture. This framework provides a basis for system designers and regulators to think beyond pre-deployment checks toward ongoing operational risk governance.