AI Agents Governance – A Field Guide

🔍 What’s Covered

This field guide breaks new ground by focusing on AI agents—autonomous systems built around foundation models that take independent actions in the world. The paper frames agents as the next major phase of AI deployment, potentially performing tasks once reserved for humans, across domains like cybersecurity, software engineering, and customer support.

The report outlines how these agents work today (often via scaffolding around LLMs like GPT or Claude), and where they’re heading. It presents performance benchmarks that show stark limitations: current agents fall short on tasks requiring long-term reasoning, planning, and reliability—especially those taking more than an hour of human time. Still, agents are already economically valuable in repetitive, short-form tasks (Klarna, Google, and Salesforce examples illustrate this).

From there, it moves into governance. The core of the guide is a proposed taxonomy of interventions grouped into five governance outcomes:

Alignment – tuning agents to match human goals
Control – enforcing behavioral boundaries and fail-safes
Visibility – making agent actions understandable and auditable
Security & Robustness – protecting agents against misuse and failure
Societal Integration – addressing long-term implications like inequality, labor disruption, and concentrated power

These are not abstract concepts—the taxonomy ties each to existing proposals or prototypes (e.g. test-time compute, sandboxing, adversarial evaluation, law-following agents). Still, most interventions are early-stage, and the guide admits that governance tools lag far behind technical development.

This is a field guide in the literal sense: a practical, exploratory document for policymakers and researchers operating in uncertainty. It’s not definitive—but it’s full of signal.

💡 Why it matters?

AI agents are not speculative anymore. They’re being trialed at scale across industry, and their autonomy introduces new risks—especially around loss of control, cascading errors, or abuse by bad actors. Most AI governance frameworks weren’t designed with agentic behavior in mind. This guide is one of the first serious attempts to define how we might structure oversight when machines plan and act with minimal human direction. It helps move the governance conversation beyond foundation models into what’s coming next.

🧩 What’s Missing?

This guide lays a solid conceptual foundation but leaves major questions open. There’s no deep dive into how existing regulatory tools (e.g. the EU AI Act or liability law) could accommodate agents. The field lacks empirical studies or case law on real-world agent failures, and the taxonomy—though helpful—is still quite high-level. There’s little on procurement standards or how public institutions should begin adopting (or rejecting) these systems safely. Also, the societal integration section could have used more concrete pathways for inclusion, equity, and institutional safeguards.

👥 Best For

This guide is ideal for AI governance researchers, public interest technologists, and regulators exploring post-LLM oversight. It’s particularly useful for those developing risk frameworks or working on assurance for emergent capabilities. It’s also a great entry point for funders scoping agent governance as a research agenda.