  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
visibility, is critical to these objectives. In this paper, we assess three
categories of measures to increase visibility into AI agents: agent
identifiers, real-time monitoring, and 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.
