Infrastructure for AI Agents (2501.10114v3)

Abstract: AI agents plan and execute interactions in open-ended environments. For example, OpenAI's Operator can use a web browser to do product comparisons and buy online goods. Much research on making agents useful and safe focuses on directly modifying their behaviour, such as by training them to follow user instructions. Direct behavioural modifications are useful, but do not fully address how heterogeneous agents will interact with each other and other actors. Rather, we will need external protocols and systems to shape such interactions. For instance, agents will need more efficient protocols to communicate with each other and form agreements. Attributing an agent's actions to a particular human or other legal entity can help to establish trust, and also disincentivize misuse. Given this motivation, we propose the concept of \textbf{agent infrastructure}: technical systems and shared protocols external to agents that are designed to mediate and influence their interactions with and impacts on their environments. Just as the Internet relies on protocols like HTTPS, our work argues that agent infrastructure will be similarly indispensable to ecosystems of agents. We identify three functions for agent infrastructure: 1) attributing actions, properties, and other information to specific agents, their users, or other actors; 2) shaping agents' interactions; and 3) detecting and remedying harmful actions from agents. We provide an incomplete catalog of research directions for such functions. For each direction, we include analysis of use cases, infrastructure adoption, relationships to existing (internet) infrastructure, limitations, and open questions. Making progress on agent infrastructure can prepare society for the adoption of more advanced agents.

• The paper argues that external agent infrastructure, distinct from internal agent alignment, is crucial for governing AI agent interactions with the environment, institutions, humans, and other agents.
• Agent infrastructure provides core functions: Attribution (identity, certification, IDs for accountability), Interaction (channels, oversight, communication protocols, commitments to shape behavior), and Response (incident reporting, rollbacks to mitigate harm).
• This external infrastructure offers practical governance levers that complement internal safety methods, helping integrate agents into society while managing risks like accountability and unintended consequences, despite challenges in interoperability and usability.

Introduction: The Need for Agent Infrastructure

As AI systems gain the ability to plan and execute complex tasks in open-ended environments—managing finances, automating customer interactions, or controlling physical systems—their potential societal impact grows significantly. These AI agents inevitably interact with existing societal structures like legal and economic systems, as well as various actors including digital service providers, humans, and other AI agents. Current AI safety and alignment approaches primarily focus on an agent's internal mechanisms (e.g., model fine-tuning, ethical guidelines encoded within the model). However, these internal methods often fall short in addressing the broader societal consequences arising from agent interactions in the real world. For instance, ensuring an agent model adheres internally to certain principles does not automatically guarantee accountability if the agent, directed by a user, performs an illegal action or causes unforeseen harm through complex interactions.

To bridge this gap, the concept of agent infrastructure becomes crucial (2501.10114). This refers to technical systems and shared protocols that exist external to individual AI agents but are designed specifically to mediate, shape, and govern their interactions with their environment and each other. Agent infrastructure involves creating new tools and protocols, as well as adapting existing systems, to manage the complexities of autonomous agent behavior. Just as foundational internet protocols like HTTPS enable secure online interactions, dedicated agent infrastructure is necessary to unlock the benefits of AI agents while managing the inherent risks they pose when operating autonomously within society (2501.10114). Focusing on mechanisms for attribution, interaction shaping, and response provides essential governance levers that complement internal alignment efforts, fostering accountability, trust, and the prevention of unintended negative consequences.

1. Defining Agent Infrastructure

Agent infrastructure comprises the technical systems and shared protocols external to AI agents that mediate and influence their interactions with their environments (2501.10114). This infrastructure shapes how agents engage with institutions (legal, economic), digital services, humans, and other agents. It operates distinctly from system-level AI safety techniques (like alignment or fine-tuning) which focus on modifying the internal workings of an agent.

A useful analogy compares agent infrastructure to traffic management systems for human drivers. Internal agent safety techniques are akin to driver training, focusing on teaching the individual agent (driver) how to behave correctly. Agent infrastructure, conversely, is like traffic lights, speed limits, and road signs – external rules and systems that govern the interactions of all agents (drivers) within the shared environment (road network).

This external infrastructure plays an indispensable role by providing mechanisms for:

• Accountability: Attributing actions to specific agents or the users controlling them.
• Risk Management: Enabling targeted interventions against harmful agents or behaviors.
• Recourse: Offering ways to undo or mitigate harm caused by agent actions.
• Trust Building: Providing verifiable information about agent capabilities and identities.

By focusing on these external mechanisms, agent infrastructure provides a scalable and adaptable layer of governance crucial for integrating advanced AI agents safely and productively into society (2501.10114).

2. Core Functions: Attribution

Attribution involves reliably assigning actions, properties, capabilities, and other relevant information to specific agents, the users controlling them, or other relevant actors (2501.10114). This function is fundamental for establishing accountability, building trust, and enabling agents to participate meaningfully within existing legal and economic frameworks. Key components include:

• Identity Binding: This process links an agent instance or its actions to a legally recognized entity (a person or organization). It often involves authentication, potentially using privacy-preserving techniques that reveal the link only when necessary (e.g., for legal recourse or dispute resolution).
  • Practical Implications: Essential for accountability (discouraging misuse like Sybil attacks), building trust (providing recourse), and applying existing laws (e.g., contract law) to agent actions. Existing systems like OAuth or KYC protocols could be adapted, but need extension for agent-specific contexts.
  • Implementation Challenges: Balancing privacy with the need for accountability requires robust cryptographic methods and carefully designed protocols.
• Certification: This involves creating, verifying, and potentially revoking verifiable claims (certificates) about an agent's properties, capabilities, or behavior. Examples include certifying the tools an agent can access, its level of autonomy, its data privacy practices, or its resistance to certain attacks (like prompt injection).
  • Practical Implications: Builds trust by allowing counterparties (humans or other agents) to verify claims before interaction. Incentivizes desirable agent properties ("race to the top" via selective interaction). Supports recourse by enabling verification of adherence to specific standards. Analogous to SSL certificates for websites.
  • Implementation Challenges: Ensuring claims are genuinely verifiable is crucial; unverifiable claims offer little value. Preventing fraudulent or misleading certificates requires robust certification authorities and transparent verification processes.
• Agent IDs: These are unique, potentially cryptographically secured identifiers for individual agent instances. They can contain or link to metadata such as certifications or bound identities.
  • Practical Implications: Serve as a fundamental tracking mechanism (like serial numbers). Support the display of associated certifications and identity bindings. Aid incident response by linking actions across platforms or sub-agents to a specific instance. Enable targeted interventions (e.g., blocking a specific malicious agent).
  • Implementation Challenges: Achieving interoperability across different platforms and developers is critical. Competing, incompatible ID schemes would fragment the ecosystem and limit the usefulness of infrastructure. Standardized formats and protocols are essential.

3. Core Functions: Interaction

This function focuses on shaping how agents interact with counterparties (services, humans, other agents) and the broader environment through external systems and protocols (2501.10114). The goal is to guide agent behavior towards safer and more productive outcomes. Components include:

• Agent Channels: These mechanisms isolate agent-generated traffic from other types of digital traffic when interacting with online services. This could involve separate APIs, dedicated IP address blocks, or specific routing protocols.
  • Practical Implications: Allows service providers and potentially regulators to monitor, manage, and apply specific rules to agent traffic at scale. Facilitates containment during security incidents (e.g., shutting down compromised agent channels during a worm spread). May offer efficiency benefits for agents interacting with services designed to handle agent traffic specifically.
• Oversight Layers: These are systems providing ongoing monitoring to detect situations requiring intervention, coupled with interfaces for authorized actors (users, managers, automated systems) to step in. Intervention could involve approving/rejecting actions, providing information, or modifying behavior.
  • Practical Implications: Enhances safety and accountability by enabling review and control over critical agent actions (e.g., preventing fraudulent transactions). Addresses agent unreliability or unexpected behavior by providing a human-in-the-loop or automated check. Creates audit trails of interventions and approvals.
• Inter-Agent Communication: This involves standardized rules, addressing systems, and technical protocols specifically designed for communication between different AI agents. It supports both point-to-point and broadcast messaging.
  • Practical Implications: Enables coordination on shared tasks (e.g., supply chain management), negotiation of interaction rules, and broadcasting of important information (e.g., security alerts). Crucial for developing complex, cooperative multi-agent systems, extending capabilities beyond individual agent interactions.
• Commitment Devices: These are mechanisms designed to help agents make credible commitments and enforce agreements, addressing collective action problems and fostering cooperation. Examples include interfaces to escrow services, assurance contracts, or protocols ensuring adherence to promises.
  • Practical Implications: Enables agents to overcome game-theoretic challenges like the tragedy of the commons (e.g., preventing over-extraction of shared resources) or funding public goods. Fosters trust and reliable cooperation in multi-agent economic or social scenarios by making agreements enforceable.

4. Core Functions: Response

The Response function deals with detecting and remedying harmful actions originating from AI agents, providing a crucial safety net (2501.10114). Key components are:

• Incident Reporting: Tools and processes allowing agents, users, and other actors to report harmful events or behaviors caused by AI agents. Agents themselves could potentially act as reporters.
  • Practical Implications: Leverages the potential ubiquity of agents to gather rich, distributed data on harms, especially for agents running locally where direct oversight is limited. Helps surface novel risks and informs improvements in safety practices. Provides data for investigating harmful events.
  • Implementation Challenges: Ensuring report accuracy, filtering false positives, and effectively analyzing large volumes of report data to derive actionable insights.
• Rollbacks: Tools and interfaces designed to reverse or undo an agent's actions, mitigating the impact of errors, unintended consequences, or malicious use.
  • Practical Implications: Allows undoing actions resulting from bugs, user error, or agent hijacking (e.g., reversing unauthorized financial transactions, unsending messages, reverting system state changes). Can help contain cascading failures or security incidents (e.g., stopping a worm by reverting affected systems). Enables riskier interactions if they are conditional on reversibility. Increases confidence in deploying agents for consequential tasks.
  • Implementation Challenges: Not all actions are easily reversible (e.g., physical actions, information disclosure). Scalability can be difficult. Reversing complex, interdependent actions might have unintended downstream consequences. Requires cooperation from the services or systems where the actions occurred.

5. Significance: Shaping Interactions and Enabling Governance

Agent infrastructure provides concrete technical mechanisms to manage and shape interactions between AI agents and the world, offering governance levers that complement internal AI alignment efforts (2501.10114). By focusing on the external points of interaction, this infrastructure can influence agent behavior without necessarily altering the core AI model itself. Its significance lies in how it mediates interactions across different domains:

• With Institutions: Identity binding and certification make agent actions legible and accountable within existing legal and economic systems. Commitment devices allow agents to participate reliably in contracts and economic agreements.
• With Digital Services: Agent channels enable services to apply specific policies, manage load, and enhance security for agent traffic. Oversight layers provide control points before actions impact external APIs or systems. Rollbacks often leverage existing service capabilities (like transaction reversal APIs).
• With Humans: Oversight layers facilitate human supervision and intervention. Certification and identity binding build human trust in interacting with agents. Incident reporting allows humans to flag problems. Rollbacks offer recourse if an agent causes harm.
• With Other Agents: Inter-agent communication protocols, agent IDs, certification, and commitment devices are specifically proposed to structure agent-to-agent interactions, promoting safety, cooperation, and efficiency.

The advantage of this external, infrastructure-based approach to governance is its potential for robustness and adaptability. Infrastructure components can often be updated or reconfigured independently of the agents themselves, providing flexibility in managing an evolving ecosystem. This is particularly valuable as agents become more autonomous and interact across diverse and complex environments.

6. Challenges and Implementation Considerations

Developing and deploying effective agent infrastructure faces several significant hurdles (2501.10114):

• Interoperability: A lack of common standards across different platforms and developers for key components like Agent IDs, communication protocols, or certification formats could lead to a fragmented ecosystem. This would severely limit the ability of agents to interact seamlessly and reduce the overall value of the infrastructure.
  • Mitigation: Fostering open standards development, collaboration between major platform providers, and potentially establishing consortia to define common APIs and protocols.
• Usability: If the infrastructure tools, protocols, and APIs are too complex or difficult for developers and end-users to integrate and utilize, adoption will likely be slow. High friction can stifle experimentation and prevent widespread use.
  • Mitigation: Focusing on developer experience (DX) and user experience (UX). Providing clear documentation, SDKs, intuitive interfaces, reference implementations, and potentially abstraction layers to simplify interaction with the infrastructure.
• Lock-in Effects: Early adoption of suboptimal or even flawed infrastructure standards could become difficult to change later due to network effects and established dependencies, similar to challenges in upgrading core internet protocols. This could hinder long-term progress or perpetuate vulnerabilities.
  • Mitigation: Designing infrastructure components with flexibility and modularity in mind. Prioritizing protocols that allow for evolution and backward compatibility. Establishing clear governance processes for updating standards. Continuous monitoring and evaluation to identify problems early.

Addressing these challenges proactively through collaborative design, standardization efforts, and a focus on usability will be critical for realizing the potential of agent infrastructure.

7. Conclusion: Towards a Robust Agent Ecosystem

The increasing capabilities and autonomy of AI agents necessitate a shift beyond purely internal alignment techniques towards a more comprehensive approach involving external governance mechanisms. Agent infrastructure, consisting of technical systems and shared protocols external to agents, provides a tangible framework for mediating and shaping agent interactions within society (2501.10114).

By focusing on the core functions of Attribution (establishing accountability and trust via identity, certification, and IDs), Interaction (shaping behavior via channels, oversight, communication protocols, and commitments), and Response (mitigating harm via incident reporting and rollbacks), this infrastructure offers practical levers for governance. It complements internal safety work by managing how agents engage with legal systems, digital services, humans, and each other.

While challenges related to interoperability, usability, and potential lock-in must be carefully managed, the proactive development and deployment of well-designed agent infrastructure are essential. It provides a pathway to harness the benefits of advanced AI agents while mitigating their risks, ultimately fostering a more robust, trustworthy, and productive agent ecosystem integrated within our societal structures.