Governance Controls for Agentic AI in Public Administration

Abstract

Background: Debate around agentic AI often emphasizes formal rules; less attention is given to the organizational conditions that determine whether those rules operate consistently.

Objective: We examined implementation relating to agentic AI and its relationship with procedural clarity, coordination, institutional capacity, and public administration.

Methods: The study surveyed 762 relevant officials, professionals, service users, or regulated actors. Questionnaire items covered implementation quality, capacity, procedural clarity, transparency, and perceived effectiveness; associations with implementation outcomes were assessed descriptively and with multivariable regression.

Results: For agentic AI, 734 complete responses were available for analysis. Consistent implementation was reported by 63%, whereas 24% identified material procedural or capacity barriers. After adjustment for organization size and jurisdiction, clearer roles, stronger staff capability, and access to formal guidance remained positively associated with implementation quality.

Conclusion: The observed pattern shows that effective governance of agentic AI depends on implementation quality, not simply the existence of regulation. This means that public-sector AI rules are unlikely to be effective without operational capacity, transparent oversight, and clearly assigned responsibility.

Study design: multi-agency governance survey.

Publication history: Originally published in print on 15 June 2026, Volume 6, Article 1. Online abstract record added on 16 September 2026. Full-text PDF will be added when the digital galley is ready.