Agentic AI systems capable of reasoning, planning, and executing actions present fundamentally distinct governance challenges compared to traditional AI models. Unlike conventional AI, these systems exhibit emergent and unexpected behaviors during runtime, introducing novel agent-related risks that cannot be fully anticipated through pre-deployment governance alone. To address this critical gap, we introduce MI9, the first fully integrated runtime governance framework designed specifically for safety and alignment of agentic AI systems. MI9 introduces real-time controls through six integrated components: agency-risk index, agent-semantic telemetry capture, continuous authorization monitoring, Finite-State-Machine (FSM)-based conformance engines, goal-conditioned drift detection, and graduated containment strategies. Operating transparently across heterogeneous agent architectures, MI9 enables the systematic, safe, and responsible deployment of agentic systems in production environments where conventional governance approaches fall short, providing the foundational infrastructure for safe agentic AI deployment at scale. Detailed analysis through a diverse set of scenarios demonstrates MI9's systematic coverage of governance challenges that existing approaches fail to address, establishing the technical foundation for comprehensive agentic AI oversight.

翻译：能够进行推理、规划与执行动作的智能体AI系统，相较于传统AI模型，呈现出根本不同的治理挑战。与传统AI不同，这些系统在运行时表现出涌现性和不可预期的行为，引入了无法仅通过部署前治理完全预见的新型智能体相关风险。为填补这一关键空白，我们提出了MI9，这是首个专为智能体AI系统的安全性与对齐性设计的全集成运行时治理框架。MI9通过六个集成组件实现实时控制：智能体风险指数、智能体语义遥测捕获、持续授权监控、基于有限状态机（FSM）的合规引擎、目标条件漂移检测以及分级遏制策略。MI9能够在异构智能体架构上透明运行，使得在传统治理方法失效的生产环境中，能够系统、安全且负责任地部署智能体系统，为大规模安全部署智能体AI提供了基础架构。通过多种场景的详细分析表明，MI9系统性地覆盖了现有方法无法应对的治理挑战，为全面的智能体AI监管奠定了技术基础。