On the Regulatory Potential of User Interfaces for AI Agent Governance
This work addresses the need for practical governance of AI agents to mitigate risks, offering a complementary approach to existing system-level safeguards.
The paper tackles the problem of governing AI agents by proposing regulation of user interfaces to enforce transparency and behavioral requirements, identifying six interaction design patterns from 22 existing systems and concluding with policy recommendations.
AI agents that take actions in their environment autonomously over extended time horizons require robust governance interventions to curb their potentially consequential risks. Prior proposals for governing AI agents primarily target system-level safeguards (e.g., prompt injection monitors) or agent infrastructure (e.g., agent IDs). In this work, we explore a complementary approach: regulating user interfaces of AI agents as a way of enforcing transparency and behavioral requirements that then demand changes at the system and/or infrastructure levels. Specifically, we analyze 22 existing agentic systems to identify UI elements that play key roles in human-agent interaction and communication. We then synthesize those elements into six high-level interaction design patterns that hold regulatory potential (e.g., requiring agent memory to be editable). We conclude with policy recommendations based on our analysis. Our work exposes a new surface for regulatory action that supplements previous proposals for practical AI agent governance.