Increased delegation of commercial, scientific, governmental, and personal
activities to AI agents -- systems capable of pursuing complex goals with
limited supervision -- may exacerbate existing societal risks and introduce new
risks. Understanding and mitigating these risks involves critically evaluating
existing governance structures, revising and adapting these structures where
needed, and ensuring accountability of key stakeholders. Information about
where, why, how, and by whom certain AI agents are used, which we refer to as
\textbf{visibility}, is critical to these objectives. In this paper, we assess
three categories of measures to increase visibility into AI agents:
\textbf{agent identifiers}, \textbf{real-time monitoring}, and \textbf{activity
logging}. For each, we outline potential implementations that vary in
intrusiveness and informativeness. We analyze how the measures apply across a
spectrum of centralized through decentralized deployment contexts, accounting
for various actors in the supply chain including hardware and software service
providers. Finally, we discuss the implications of our measures for privacy and
concentration of power. Further work into understanding the measures and
mitigating their negative impacts can help to build a foundation for the
governance of AI agents.

增加商业、科学、政府和个人活动向 AI 代理地委托，可能加剧现有社会风险并引入新风险。了解和缓解这些风险需要对现有管理结构进行批判性评估，在需要时进行修订和调整这些结构，并确保关键利益相关者的责任追究。对特定 AI 代理的使用地点、原因、方式和使用者的信息，也就是所谓的 “可见性”，对于实现这些目标至关重要。本文评估了增加对 AI 代理可见性的三类措施：代理标识符、实时监测和活动记录。对于每个措施，我们概述了可能的实施方式，这些方式在侵入性和信息性方面存在差异。我们分析了这些措施如何适用于从集中式到分散式部署环境的各种情景，并考虑了供应链中的各种参与者，包括硬件和软件服务提供商。最后，我们讨论了我们措施对隐私和权力集中的影响。进一步研究这些措施并缓解其负面影响，有助于为 AI 代理的治理奠定基础。