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Visibility into AI Agents

Alan Chan, Carson Ezell, Max Kaufmann, Kevin Wei, Lewis Hammond, Herbie Bradley, Emma Bluemke, Nitarshan Rajkumar, David Krueger, Noam Kolt, Lennart Heim, Markus Anderljung

TL;DR

Visibility into AI agents addresses governance challenges arising from increasingly autonomous, tool-using agents operating with long-horizon goals. The paper proposes three measures—agent identifiers, real-time monitoring, and activity logs—and analyzes their applicability across centralized and decentralized deployments, considering actors across the supply chain including hardware and software providers. It discusses privacy implications and the potential for concentration of power, arguing for careful study and possible voluntary standards before mandated adoption. Overall, the work provides a foundation for governance of AI agents by enabling accountability, oversight, and safer deployment through visibility.

Abstract

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.

Visibility into AI Agents

TL;DR

Visibility into AI agents addresses governance challenges arising from increasingly autonomous, tool-using agents operating with long-horizon goals. The paper proposes three measures—agent identifiers, real-time monitoring, and activity logs—and analyzes their applicability across centralized and decentralized deployments, considering actors across the supply chain including hardware and software providers. It discusses privacy implications and the potential for concentration of power, arguing for careful study and possible voluntary standards before mandated adoption. Overall, the work provides a foundation for governance of AI agents by enabling accountability, oversight, and safer deployment through visibility.

Abstract

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.
Paper Structure (28 sections, 2 figures)

This paper contains 28 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Risks of AI Agents
  3. Malicious Use
  4. Overreliance and Disempowerment
  5. Delayed and Diffuse Impacts
  6. Multi-Agent Risks
  7. Sub-Agents
  8. The Case for Visibility into AI Agents
  9. Contributions
  10. Definitions
  11. Measures to Improve Visibility
  12. Agent Identifiers
  13. Types of Agent Identifiers
  14. Attaching Additional Information to Agent Identifiers
  15. Real-Time Monitoring
  16. ...and 13 more sections

Figures (2)

  • Figure 1: We illustrate how our main terms in \ref{['sec:definitions']} interact with each other. Deployers are in red and encompass the agents box to denote the fact that our paper focuses on agents that are run by deployers and served to users. Developers build agents (or an underlying system) and deployers serve instances of agents to users. Since deployers run agents, the inputs and outputs of agents are by default visible to the deployer, which facilitates the measures that we discuss in \ref{['sec:measures']}.
  • Figure 2: We illustrate the flow of information for our measures in \ref{['sec:measures']}.