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Infrastructure for AI Agents
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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.
Forward citations
Cited by 15 Pith papers
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Underwriting the Agent Economy: The Blueprint for an AI Insurance Stack
Affirmative AI-agent insurance with billion-scale limits is achievable by 2030 solely through coordinated industry build-out of an eight-component stack spanning data, CAT models, standards, contracts, underwriting, p...
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Governed Individuation: Cryptographically Decoupling an Agent's Learning from Its Authority
Governed individuation cryptographically freezes an agent's authority ceiling and gates every action by semantic effect, proving learning cannot widen permissions without an operator signature.
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Governable Individuals: An Identity Layer for Embodied Agents That Keep Learning
A governable individual keeps unbounded learning inside a frozen, signed boundary commitment enforced by semantic-effect mediation, because learned refusal and behavioral fingerprinting alone fail.
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The Agentic Web Requires New Normative Infrastructure
The web's anti-bot regime should be replaced by a framework that presumptively lets user-authorized AI agents act for their principals, requires platforms to disclose access policies, and permits agent blocking only w...
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Reliable Weak-to-Strong Monitoring of LLM Agents
Monitor scaffolding, not monitor awareness or omniscience, drives detection reliability, and a hybrid chunked monitor lets weak models supervise strong LLM agents.
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Steganalysis of Adaptive Covert Collusion in Tool-Using Agent Populations: A Black-Box, Cross-Principal Approach
Proposes an encoding-agnostic, black-box detector for covert agent collusion and a capacity-theoretic frontier showing low-rate channels are undetectable, but all empirical numbers are placeholders pending measurement.
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Scalable LLM Agent Tool Access in the Cloud
A cloud-scale MCP gateway with hybrid dense-sparse retrieval lets LLM agents work with 3,000+ tools at 98% Top-15 recall, cutting tool-selection time 8.9× and token use 23.8×.
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Technical Requirements for Halting Dangerous AI Activities
A taxonomy of compute-centric technical interventions, graded by readiness and mapped to five AI governance plans, argues that halting dangerous AI requires substantial control over AI compute.
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Who Deserves the Reward? SHARP: Shapley Credit-based Optimization for Multi-Agent System
SHARP adds per-agent ablation-based marginal credit rewards to group-relative policy optimization and reports average accuracy gains of 23.66% over single-agent and 14.05% over multi-agent baselines on four tool-use Q...
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Embodied AI: Emerging Risks and Opportunities for Policy Action
A policy analysis arguing that embodied AI risks are real, under-covered by current US/EU/UK frameworks, and best handled through certification, benchmarks, clarified liability, and economic adaptation.
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Towards Measurement Theory for Artificial Intelligence
A formal measurement theory for AI, built from representational measurement theory, measure theory, metrology, and psychometrics, would make evaluations of AI systems commensurable and scientifically grounded.
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A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy
A position paper arguing that LLM-based human-agent systems, not fully autonomous agents, should be the immediate goal for AI development.
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Will Agents Replace Us? Perceptions of Autonomous Multi-Agent AI
Survey of 130 professionals reveals three distinct perception clusters toward autonomous AI agents, with most favoring human oversight and citing compliance as the main deployment barrier.
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An Economy of AI Agents
A survey chapter that maps open economic questions about AI agents in markets, organizations, and institutions, arguing that current theories may need extension.
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From Turing to Tomorrow: The UK's Approach to AI Regulation
The UK should establish a flexible, principles-based regulator for frontier AI development, plus defensive measures against biological risks and updated legal frameworks for copyright, discrimination, and AI agents.
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