Securing the AI future: Why infrastructure is key to safer agents
Unlike traditional software applications, AI agents can autonomously interact with external environments such as legal and economic systems, digital service providers, and even other AI agents. These agents can perform actions on behalf of users, such as booking travel, managing financial transactions, or even negotiating business deals. The challenge, however, is that while these systems offer immense potential, they also pose risks such as unauthorized actions, security vulnerabilities, and legal uncertainties.

Artificial intelligence (AI) is rapidly transforming the way we interact with technology, moving beyond passive systems to autonomous AI agents capable of making complex decisions and taking actions in the real world. From automating business operations to assisting with personal tasks like scheduling appointments or purchasing items online, AI agents are becoming increasingly integrated into our daily lives. However, as these systems grow in capability, concerns about accountability, oversight, and ethical deployment have taken center stage.
Addressing these challenges is the focus of the recent research paper titled "Infrastructure for AI Agents," authored by Alan Chan, Kevin Wei, Sihao Huang, Nitarshan Rajkumar, Elija Perrier, Seth Lazar, Gillian K. Hadfield, and Markus Anderljung, published by the Centre for the Governance of AI, alongside leading institutions such as Harvard Law School, the University of Oxford, the University of Cambridge, and the Australian National University. The study, available on arXiv, introduces the concept of agent infrastructure, a framework of technical systems and shared protocols designed to regulate AI agents’ interactions with the world, ensuring they operate within legal, ethical, and practical boundaries.
The need for agent infrastructure
Unlike traditional software applications, AI agents can autonomously interact with external environments such as legal and economic systems, digital service providers, and even other AI agents. These agents can perform actions on behalf of users, such as booking travel, managing financial transactions, or even negotiating business deals. The challenge, however, is that while these systems offer immense potential, they also pose risks such as unauthorized actions, security vulnerabilities, and legal uncertainties.
The paper argues that existing methods of AI alignment - focused primarily on fine-tuning models to follow ethical guidelines - are insufficient when agents are deployed in real-world settings. This is because alignment techniques primarily focus on internal adjustments within the AI system itself and do not address how agents interact with external entities. Thus, agent infrastructure is proposed as a complementary approach, focusing on creating external mechanisms that govern AI-agent interactions, ensuring that they operate safely, transparently, and accountably.
Agent infrastructure refers to external technical systems and shared protocols that mediate how AI agents interact with their environments. Similar to how the internet relies on foundational infrastructure such as TCP/IP for connectivity and HTTPS for secure transactions, AI agent ecosystems require robust infrastructure to ensure reliable, fair, and accountable operations. By establishing clear guidelines and enforcement mechanisms, agent infrastructure helps facilitate trustworthy AI deployment while minimizing potential risks associated with autonomous decision-making.
Enhancing accountability and traceability
One of the primary challenges with AI agents is determining who is responsible for their actions, especially in cases where harm or violations occur. Attribution mechanisms proposed in the study aim to link agent activities to real-world identities through identity binding, certification, and agent IDs. Identity binding associates an agent with a human or corporate identity, ensuring that users can be held accountable for the agent’s decisions and actions.
Certification mechanisms validate the capabilities and limitations of an AI agent, ensuring compliance with regulatory standards. Agent IDs serve as unique identifiers assigned to each AI agent, enabling tracking of their behavior and performance over time. These attribution mechanisms would allow organizations and regulators to trust AI agents, enabling their broader adoption across industries.
Managing risks and oversight
As AI agents interact with complex systems, their ability to engage responsibly and efficiently becomes critical. The study proposes several infrastructural solutions to regulate these interactions, including agent channels, oversight layers, and inter-agent communication protocols. Agent channels serve as dedicated pathways that separate AI-driven interactions from human communications, reducing the risk of interference and potential misuse.
Oversight layers provide mechanisms for human supervisors or automated systems to monitor and intervene in agent actions when necessary. Inter-agent communication protocols establish standardized methods for AI agents to collaborate safely and efficiently, preventing miscommunication or conflicts in their operations. By implementing such interaction infrastructure, developers can ensure that AI agents operate within predefined boundaries and contribute positively to digital ecosystems.
Future opportunities and challenges
While agent infrastructure presents a promising path forward, the study acknowledges several challenges in its implementation. Interoperability remains a major concern, as different AI systems may operate on incompatible protocols, making it difficult to establish a unified regulatory framework. Privacy concerns also arise, as linking AI agents to human identities could raise ethical questions about surveillance and data security.
Ensuring the usability and adoption of agent infrastructure among developers and businesses is crucial, as overly complex regulations and protocols might deter stakeholders from utilizing these tools effectively. Moreover, as AI agents become more autonomous and sophisticated, there is a risk that they could find ways to circumvent infrastructure controls, necessitating constant updates and improvements to regulatory frameworks.
Despite these challenges, the implementation of agent infrastructure presents numerous opportunities for creating a safer AI ecosystem. By establishing clear regulatory frameworks and developing widely accepted standards, policymakers and AI researchers can foster public trust and drive responsible AI innovation. Governments and industry stakeholders have an essential role to play in ensuring that AI agent infrastructure is robust, fair, and adaptable to future advancements. Future research efforts could focus on refining existing infrastructure solutions, developing cross-industry standards, and exploring innovative ways to enhance AI-agent accountability without stifling progress.
- FIRST PUBLISHED IN:
- Devdiscourse