As companies enter the race for agentic AI adoption, fears surface around agentic autonomy and its subsequent risks. These fears compound as companies scale their agentic AI adoption with low-code applications, without a comparable scaling in their governance processes and expertise resulting in a phenomenon known as "Agent Sprawl". While shadow AI tools can help with agentic discovery and identification, few observability tools offer insights into the agents' configuration and settings or the decision-making process during agent-to-agent communication and orchestration. This paper explores AI governance professionals' concerns in enterprise settings, while offering design-time and runtime explainability techniques as suggested by AI governance experts for addressing those fears. Finally, we provide a preliminary prototype of an Agentic AI Card that can help companies feel at ease deploying agents at scale.

翻译：随着企业争相采用智能体人工智能，对智能体自主性及其相关风险的担忧逐渐浮出水面。当企业通过低代码应用扩展智能体AI部署，而治理流程与专业知识未能同步扩展时，这些担忧进一步加剧，从而引发被称为"智能体蔓延"的现象。尽管影子AI工具可能有助于智能体的发现与识别，但很少有可观测性工具能揭示智能体的配置、设置，或智能体间通信与编排过程中的决策机制。本文探讨了企业环境中AI治理专业人士的关切，同时提出了AI治理专家建议的应对方案——包括设计时与运行时可解释性技术。最后，我们提供了一款智能体AI卡片的初步原型，旨在帮助企业能够放心地大规模部署智能体。