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New development: Principal–agent issues when governments embrace AI agents

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

The growing presence of agentic AI in public governance is transforming the once familiar, human-bound principal–agent relationship into a multi-directional socio-technical challenge. This article provides a structured analysis of these challenges by dissecting how AI-related agency problems manifest across three critical delegation interfaces: trust delegation between human users (principals) and AI agent systems; hierarchical delegation between senior officials (principals) and lower-level officials (agents) augmented by AI agents; and contractual delegation where government officials (principals) outsource AI development to private firms (agents). At each interface, the authors clarify how delegation drift may arise due to the intensified information asymmetry and goal misalignment, while creating new accountability gaps. Building on this analysis, the article concludes with a discussion on governing AI agents and proposes a forward-looking research agenda for public administration scholars. © 2026 Informa UK Limited, trading as Taylor & Francis Group.
Original languageEnglish
Number of pages6
JournalPublic Money and Management
DOIs
Publication statusOnline published - 30 Mar 2026

Funding

The work described in this article was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CityU 15513723).

Research Keywords

  • Artificial bureaucrats
  • digital government
  • generative AI (GenAI)
  • principal–agent problem
  • public governance
  • public services

Publisher's Copyright Statement

  • COPYRIGHT TERMS OF DEPOSITED POSTPRINT FILE: This is an Accepted Manuscript of an article published by Taylor & Francis in Public Money and Management on 30 Mar 2026, available online: https://www.tandfonline.com/doi/full/10.1080/09540962.2026.2642814.

RGC Funding Information

  • RGC-funded

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