# Infrastructure for AI Agents

**Authors:** Alan Chan, Kevin Wei, Sihao Huang, Nitarshan Rajkumar, Elija Perrier, Seth Lazar, Gillian K. Hadfield, Markus Anderljung  
**arXiv:** [2501.10114](https://arxiv.org/abs/2501.10114)  
**Date:** 2025-01-17  
**Summary page:** https://www.summarizepaper.com/en/paper/2501.10114v1/

## Key points

- The need for tools to unlock benefits and manage risks of AI systems in open-ended environments is increasing.
- Current tools are insufficient in shaping interactions between AI agents and existing institutions and actors.
- The concept of agent infrastructure is proposed as external technical systems and shared protocols to mediate and influence AI agents' interactions with their environments.
- Agent infrastructure includes new tools or modifications of existing ones, such as leveraging authentication systems like OpenID for accountability.
- Three key functions of agent infrastructure are attributing actions to specific agents/users, shaping interactions, and detecting/remedying harmful actions.
- Various infrastructural solutions are proposed to achieve these functions while considering use cases, adoption challenges, limitations, and unresolved questions.
- Isolating traffic could be a viable strategy for incident management by allowing service providers to shut down agent channels without affecting human channels to contain malicious content spread.
- Effective infrastructural solutions are crucial for responsible AI development and deployment in a rapidly evolving technological landscape.

## Summary

In a world where AI systems are increasingly capable of planning and executing interactions in open-ended environments, the need for tools to unlock their benefits and manage their risks is becoming more apparent. Current tools fall short in shaping how these AI agents interact with existing institutions and actors, such as legal and economic systems, digital service providers, humans, and other AI agents. For instance, alignment techniques do not guarantee accountability when users instruct agents to perform illegal actions. To address this gap, the concept of agent infrastructure is proposed: technical systems and shared protocols external to agents that mediate and influence their interactions with their environments. Agent infrastructure encompasses new tools as well as reconfigurations or extensions of existing ones. For example, protocols tying users to agents could leverage existing authentication systems like OpenID to enhance accountability. Just as the Internet relies on infrastructure like HTTPS for secure communication, agent infrastructure is deemed essential for ecosystems of agents. Three key functions of agent infrastructure are identified: attributing actions and information to specific agents or users, shaping agents' interactions, and detecting/remedying harmful actions. To advance progress in this area, various infrastructural solutions are proposed to achieve each function while considering use cases, adoption challenges, limitations, and unresolved questions. By developing robust agent infrastructure that can attribute actions accurately, shape interactions effectively, and detect/remedy harmful behavior promptly,society can better prepare for the adoption of more advanced AI agents. Furthermore,

considerations around macroeconomic effects resulting from high volumes of agent activity highlight the importance of managing incidents effectively. Isolating traffic could be a viable strategy for incident management by allowing service providers to shut down agent channels without affecting human channels in order to contain the spread of malicious content. In conclusion,is crucial for ensuring responsible AI development and deployment in a rapidly evolving technological landscape. By addressing issues related to accountability, interaction shaping, incident management, and macroeconomic effects through effective infrastructural solutions, we can pave the way for a more sustainable integration of advanced AI technologies into society.