The emergence of Agentic AI systems has outpaced the architectural thinking required to operate them effectively. These agents differ fundamentally from traditional software: their behavior is not fixed at deployment but continuously shaped by experience, feedback, and context. Applying operational principles inherited from DevOps or MLOps, built for deterministic software and traditional ML systems, assumes that system behavior can be managed through versioning, monitoring, and rollback. This assumption breaks down for Agentic AI systems whose learning trajectories diverge over time. This introduces non-determinism making system reliability a challenge at runtime. We argue that architecting such systems requires a shift from managing control loops to enabling dynamic co-evolution among agents, infrastructure, and human oversight. To guide this shift, we introduce CHANGE, a conceptual framework comprising six capabilities for operationalizing Agentic AI systems: Contextualize, Harmonize, Anticipate, Negotiate, Generate, and Evolve. CHANGE provides a foundation for architecting an AgentOps platform to manage the lifecycle of evolving Agentic AI systems, illustrated through a customer-support system scenario. In doing so, CHANGE redefines software architecture for an era where adaptation to uncertainty and continuous evolution are inherent properties of the system.

翻译：Agentic AI系统的出现速度已超过有效运行它们所需的架构思考。这些智能体与传统软件存在根本差异：其行为并非在部署时固定，而是持续由经验、反馈和上下文动态塑造。沿用从DevOps或MLOps继承的操作原则（这些原则为确定性软件和传统ML系统构建）时，会默认系统行为可通过版本控制、监控和回滚进行管理。这种假设对Agentic AI系统并不适用，因为其学习轨迹会随时间不断分化。这引入了非确定性，使得系统可靠性在运行时成为严峻挑战。我们认为，构建此类系统需要从管理控制循环转向促进智能体、基础设施与人工监督之间的动态协同演化。为引导这一转变，我们提出CHANGE概念框架，包含实现Agentic AI系统可操作的六项核心能力：情境化(Contextualize)、协调化(Harmonize)、预见化(Anticipate)、协商化(Negotiate)、生成化(Generate)与演化化(Evolve)。CHANGE为构建AgentOps平台提供了理论基础，以管理持续演化的Agentic AI系统全生命周期，并通过客户支持系统场景进行例证。由此，CHANGE重新定义了软件架构的范式，使其适应系统内在具备不确定性适应与持续演化能力的时代。