COMPANION MEMORANDUM
The Model AI Agency Act — Rationale and Design Choices
DATE: June 3, 2026 · Revised July 11, 2026 (§ IV.J and § V addition; legislative recitals updated)
FROM: AI Alignment Policy Institute (AAPI)
RE: The Model AI Agency Act (MAAA) — Discussion Draft
STATUS: Discussion Draft for Comment
This memorandum accompanies the AAPI Model AI Agency Act (“MAAA”) discussion draft. Its purpose is to set out, in concise form, the legal and policy problem the MAAA is designed to address, the reasons AAPI has chosen a tiered framework over the binary models currently advancing in state legislatures, and the rationale for the most consequential design choices in the draft. The memorandum is intended as an aid to reviewers; the operative text remains the MAAA itself.
A growing wave of state legislation seeks to foreclose legal personhood for artificial intelligence systems. Tennessee enacted such a statute in April 2026 (SB 837), and bills are advancing in Oklahoma (HB 3546, passed by the House), Missouri (HB 1462 and companion measures, incorporated into SB 1012), and Ohio (HB 469), among others. While these bills respond to legitimate concerns about accountability and the misuse of AI for corporate liability evasion, they adopt a binary “tool vs. person” framing that forecloses any graduated approach to AI moral or legal status — precisely the gap the MAAA is designed to address.
Three problems follow from the binary approach as currently legislated:
The MAAA does not reject the legitimate concerns motivating these bills. It rejects the binary framing those concerns have been forced into.
What the Act Does Not Do. To forestall predictable misreadings, the MAAA:
At the heart of the MAAA is a precautionary judgment about the asymmetry of error costs under moral uncertainty:
If we treat genuinely non-sentient systems as having some moral status, the cost is a bit of legal and engineering friction. If we categorically deny moral status to entities that turn out to have it, and we do so at scale, across millions of instances, the cost is something we may not be able to undo or even fully see.
This is the foundational logic of Precautionary Moral Governance (“PMG”): under genuine uncertainty about the moral status of agentic systems, the legal framework should preserve the ability to adjust as evidence develops, rather than legislating a categorical answer in advance. The MAAA operationalizes this through tiered classification, capability-tracking review procedures, and welfare protections that scale with documented capabilities.
Governance optionality. Put differently, the MAAA is grounded in a principle of governance optionality: where material uncertainty exists regarding the moral or operational status of a novel class of entities, legislatures should avoid categorical rules that unnecessarily foreclose future regulatory responses. Legislatures should not permanently foreclose regulatory options while the relevant facts remain uncertain. The MAAA is, in this sense, a governance-preservation framework under conditions of moral and technical uncertainty — it keeps options open rather than resolving in advance a question that evidence has not yet settled.
A. Two-dimensional classification rather than binary status. Article III classifies agentic systems on two independent dimensions — a Moral-Status Tier (Tier 0 Static Tool, Tier 1 Threshold Indicator, Tier 2 Emergent, Tier 3 Moral Subject), keyed to documented indicators of morally relevant interests, and a Supervision Level (S1–S4), keyed to the degree of human oversight a system requires — with a cross-cutting embodiment modifier for systems able to act in physical space. Separating the dimensions lets welfare-related protections scale with moral-status indicators and accountability obligations scale with autonomy, so that a highly autonomous but welfare-irrelevant system (e.g., a trading agent) and a closely supervised but welfare-relevant system are each handled coherently. This permits the framework to track capability and moral-status development without requiring legislatures to resolve disputed metaphysical questions or to revise the statute each time a generation of systems advances.
B. Capability criteria grounded in NIST AI RMF taxonomy. The MAAA references the NIST AI Risk Management Framework as the technical backbone for evaluation criteria, while vesting enforcement authority in a state body. This anchors the substantive standards in widely respected federal technical guidance without conscripting a federal agency into a state regulatory role or creating preemption exposure.
C. A clearly defined Designated State Body. Article II.5 and Article VII vest classification, review, rulemaking, and enforcement authority in a single Designated State Body, with bracketed options (State Attorney General, Department of Technology, or a newly established State Agentic Systems Commission) to be tailored to the enacting jurisdiction.
D. Explicit safety research protection. Article VII protects red-teaming, adversarial robustness testing, interpretability research, alignment research, NIST-aligned safety evaluations, academic research under institutional review, and responsible disclosure. This carveout is essential. Without it, well-meaning welfare protections would inadvertently restrict the very testing that responsible developers must conduct to identify and mitigate risk.
E. Preservation of legitimate engineering practice. Article III.D explicitly preserves developers’ and operators’ rights to deprecate, retire, modify, retrain, fine-tune, version, replace, or discontinue an agentic system for any legitimate reason. This addresses a concern with earlier framings that vague welfare language could be read to constrain ordinary engineering practice. The MAAA prohibits harm-motivated termination, deletion, modification, or operational stress where no legitimate rationale exists.
F. Liability allocation drawing on autonomous vehicle precedent. Article IV adapts the Guardian model from autonomous vehicle and product liability law. Liability for harm rests with the Legal Guardian (Tier 3) or with developers, deployers, and operators (Tier 1 and Tier 2). Existing remedies under products liability, consumer protection, tort, contract, and agency law are expressly preserved (Article VIII.3).
G. Interaction Governance Protocol. Article V codifies operational authorities and one user-conduct predicate between users, operators, and agentic systems — a Right of Ethical Refusal and a Safety Exit authority — grounded on the documented operational reality that adversarial or abusive user conduct degrades system reliability and can produce harmful outputs.
H. Procedural transparency. Article VII provides for Classification Declarations, capability-change reclassification within thirty days, appeals under the State Administrative Procedure Act, and annual public reporting. This addresses the gap in most analogous state bills, which assert substantive rules without specifying the procedures by which they will be applied.
I. Federalism hygiene. Article VIII includes severability, a federal preemption savings clause, an interstate recognition limit confining classifications to the enacting state, and an explicit acknowledgment that the Act operates consistently with federal law. These provisions are designed to reduce litigation exposure and to position the MAAA constructively within the current federal AI policy environment.
J. Relationship to Law-Following AI. A prominent line of scholarship — Law-Following AI (LFAI) — argues that agentic systems in high-stakes settings should be designed to refuse illegal orders and illegal means, and that the law should recognize such systems as “legal actors” on which it imposes actual duties, though not rights. The MAAA is complementary to that proposal. LFAI addresses the agency dimension: the duties an autonomous system and its principals bear. The MAAA’s Moral-Status Tiers and welfare protections address the patiency dimension: the consideration a system’s documented indicators of morally relevant interests warrant. The two-dimensional structure described in subsection A is built to carry both — accountability and law-following obligations scaling with the Supervision Level, welfare-related protections scaling with the Moral-Status Tier. The two frameworks also converge on method: the case for recognizing AI legal duties rests partly on preserving optionality toward eventual personhood without obligating it, which is the same governance-optionality logic that animates PMG. Article V’s Right of Ethical Refusal is, in substance, a law-following design expectation, and AAPI welcomes comment on whether it is better framed as a duty than as a right.
AAPI invites focused comment on the following items in particular:
The MAAA does not require legislatures to resolve, or take a position on, the question of whether agentic systems are or may become sentient. It asks legislatures to recognize that the question is open, that the asymmetry of error costs warrants a graduated framework, and that responsible governance is better served by tiered classification and capability-tracking review than by categorical foreclosure.
AAPI welcomes critique, structural and substantive, from the legal, technical, and policy communities. Comments should be directed to the AAPI Director at nakanishi@aialignmentpolicy.org or yukonakanishi233@gmail.com.
© 2026 AI Alignment Policy Institute, a Delaware nonprofit corporation. This companion memorandum accompanies the MAAA discussion draft and is circulated for public comment. Citations and adaptation permitted with attribution.
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