如何设计能自主运行又受控的AI系统，让企业安全扩展智能工作。
Governance by Design: Architecting Agentic AI for Organizational Learning and Scalable Autonomy
- 通过架构与流程设计约束AI行为边界，明确权限与数据使用规则。
- 在真实企业环境中实现分阶段部署，保障安全性与可追溯性。
- 为组织学习和规模化提供可复制的治理框架，适合大型企业参考。
具有自主目标规划与工具调用能力的代理型AI系统正从实验走向企业应用。这一转变带来实施、扩展与治理之间的张力：组织需要通过自动化提升知识与协作效率，但必须确保可问责性、安全性、成本控制及责任归属。本文基于某大型IT服务公司在2025年对集成企业工具的代理型系统的开发与分阶段部署的深入案例研究，发现治理通过具体的架构与工作安排实现——包括限定系统可执行的操作、允许使用的工具与数据、记忆管理方式，以及性能迭代更新机制。研究提炼出七条经验，说明如何在实际运营与规模化过程中将有效治理嵌入代理型AI系统中。
原文摘要 · Abstract (English)
Agentic AI systems - systems that can pursue goals through multi-step planning and tool-mediated action with limited direct supervision - are moving from experimental prototypes to enterprise deployments. This transition introduces tensions in implementation, scaling, and governance: organizations seek scalable autonomy for knowledge and coordination work, yet must preserve accountability, safety, cost control, and responsibility as systems initiate actions, access enterprise data, and evolve through iterative updates. Building on an in-depth qualitative case of a large IT services company's 2025 development and staged rollout of an agentic system integrated with enterprise tools; we show that governance is implemented through concrete architectural and working arrangements that determine what the system is allowed to do, which tools and data it can use, how memory is handled, and how performance improvements are introduced over time. We then distill seven lessons that explain how to build effective governance into agentic AI during operationalization and scaling.
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