为应对企业规模化部署智能体带来的治理焦虑，提出可扩展的解释性方案。
Agentic Explainability at Scale: Between Corporate Fears and XAI Needs
- 从设计与运行时双角度提供智能体可解释性技术。
- 揭示智能体间通信与编排中的决策机制，缓解管理失控担忧。
- 适合关注智能体治理与可信部署的企业安全与合规团队。
随着企业加速采用智能体人工智能，对其自主性的担忧日益加剧，尤其在低代码应用快速推广而治理能力未同步提升的情况下，导致‘智能体泛滥’现象。尽管影子AI工具可用于发现和识别智能体，但现有可观测性工具仍难以揭示其配置、设置及智能体间通信与协同过程中的决策逻辑。本文调研了企业环境中AI治理专业人士的核心关切，并基于专家建议，提出设计阶段与运行阶段的可解释性方法。最后，我们构建了一个初步原型——智能体卡片（Agentic AI Card），助力企业更安心地实现智能体的大规模部署。
原文摘要 · Abstract (English)
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.
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