⚡ Quick Summary
Partnership on AI (PAI) lays out a practical roadmap to govern AI agents—systems that plan and act via tools and APIs, not just generate text. The report argues that impacts are still uncertain, so governments should prioritize evidence-building over prescriptive rules. It structures a research agenda around three requirements: (1) understand the technology and policy landscape, (2) understand risks and opportunities, and (3) understand interventions. The headline recommendation: stand up sandboxes and testbeds to observe real behaviors, generate data, and avoid premature, misaligned regulation. The agenda includes 12 top-level and 45 sub-questions and highlights agent visibility, post-deployment monitoring, and trust infrastructure (IDs, attribution, rollbacks).
🧩 What’s Covered
PAI defines AI agents as model-scaffolded systems that can reason, plan, and take sequences of actions with minimal human oversight. The diagram on page 5 tiers “environmental interaction” from Level 0 (read-only) to Level 5 (unconstrained, tool-acquiring) and anchors today’s systems mostly at Levels 1–3, while anticipating 4–5 as the emerging frontier. This framing underpins governance thresholds tied to autonomy, impact, and goal complexity.
Requirement 1 — Technology & policy landscape. Policymakers need a shared model of agent components, actors, and value chains to map existing frameworks (tort/product liability, sectoral rules, NIST AI RMF, EU AI Act) and avoid duplicative or conflicting rules. Sub-questions probe which characteristics trigger additional governance, how existing laws bite for agentic behaviors, and how jurisdictions are converging or fragmenting. A running hypothetical—an unconstrained Level 5 tax-filing agent—illustrates liability, privacy, and cross-border frictions.
Requirement 2 — Risks & opportunities. The agenda steers away from generic risk catalogs and instead asks whereagents will actually be used, who benefits, how big the gains could be (e.g., public sector, health, education), and which hazards dominate (multi-agent dynamics, failures, misuse, labor displacement, trust/information harms). It emphasizes separating issues markets will fix (e.g., reliability if visible to users) from those demanding intervention (e.g., systemic externalities, privacy, equity).
Requirement 3 — Interventions. The report prioritizes governance mechanisms that produce observable evidence:
- Sandboxes/testbeds/live testing to evaluate capabilities, policy feasibility, and real-world impacts under supervision.
- Visibility & monitoring: documentation, logging, incident reporting, and population-level metrics (e.g., rates of agent-agent vs. human-agent interactions).
- Trust infrastructure: agent IDs, attribution, audit trails, circuit breakers/rollbacks, authenticated “agent channels,” and registries—ideally standardized internationally.
- Licensing, audit, assurance: task-specific evaluations correlated to real-world outcomes, potentially tiered by capability/scope.
- Demand-/supply-side levers to spread beneficial adoption (education, interoperability, data access).Implementation cautions include preventing regulatory capture and coordinating mandates across agencies and borders.
Finally, PAI flags its own next steps on international governance, human connection, real-time failure detection, and labor impacts, signaling a multiyear program to operationalize these questions.
💡 Why it matters?
Agentic systems won’t just advise—they’ll act. That shifts risk from “bad suggestions” to automated, scalable actionswith legal, financial, and safety consequences. Acting early to build monitoring capacity, testbeds, and trust infrastructure lets governments learn quickly, steer markets toward safer designs, and reserve prescriptive rules for when evidence is strong—avoiding both paralysis and overreach. This agenda is a pragmatic bridge from hype to measurable governance.
❓ What’s Missing
- Implementation playbooks: detailed sandbox designs by sector (finance, health), success metrics, and staffing models.
- Comparative law analysis beyond pointers: concrete mappings of how liability, agency, and consumer protections attach across key jurisdictions.
- Quantification: methods to size benefits/harms and detect systemic risks (e.g., benchmark-to-impact validation).
- Failure taxonomies for Levels 4–5 with triggers for circuit breakers/rollbacks.
- Anti-capture mechanics for sandboxes (transparency, public interest representation).
- Procurement & assurance pathways for governments to adopt certified agents while enforcing logging/attribution by default.
👥 Best For
Regulators and ministries (digital, finance, labor, health), standards bodies, public-interest researchers, think tanks, and industry policy/assurance teams seeking a concrete research backlog to make agent governance evidence-based, interoperable, and enforceable.
📄 Source Details
- Title: Preparing for AI Agent Governance — A research agenda for policymakers and researchers
- Authors: Jacob Pratt, Thalia Khan; Partnership on AI (23 pp.)
- Method: literature review + workshop with 30+ cross-sector experts; outputs: 3 requirements, 12 top-level and 45 sub-questions; emphasis on sandboxes/testbeds, visibility/monitoring, trust infrastructure, and assurance. The figure on p.5 visualizes levels of agent influence (0–5).
📝 Thanks to
Partnership on AI; authors Jacob Pratt & Thalia Khan; and the workshop contributors from academia, civil society, regulators, and industry who shaped the agenda.
