Agentic AI for Entitlement-spending Reform: A Public-sector Decision-intelligence Framework

Abstract

Entitlement spending reform is often framed primarily as a fiscal and political challenge, but scholarship on implementation, administrative burden, digital government, and public-sector governance suggests that its success also depends on administrative capacity. Governments must not only revise benefit rules and expenditure commitments, but also implement, monitor, and adapt those reforms across complex, high-volume administrative systems. Fragmented records, siloed workflows, retrospective oversight, and weak feedback loops can undermine reform even where statutory design is formally sound.This article develops a governance-oriented conceptual framework for understanding how agentic AI might support entitlement spending reform in bounded and institutionally defensible ways. Here, agentic AI is defined narrowly as a supervised decision-support and process-support architecture capable of multi-step planning, structured context retention, tool invocation, workflow coordination, and monitored interaction with human officials under explicit legal and organizational constraints. It does not refer to autonomous adjudication or independent machine determination of benefit rights.Methodologically, the article relies on a structured conceptual synthesis of interdisciplinary scholarship and policy-relevant materials spanning entitlement reform, welfare administration, digital government, public-sector artificial intelligence, and algorithmic governance. It is not a formal systematic review, nor does it evaluate a deployed system. Its contribution is therefore analytic and framework-building rather than empirical validation of effectiveness.From this synthesis, the article identifies four functional dimensions for bounded administrative use: data-driven analysis, workflow orchestration, policy simulation, and continuous monitoring. It further argues that public-sector uses must be classified by practical administrative effect, distinguishing low-impact support functions from rights-impacting functions that may affect access, burden, timeliness, scrutiny, or appeal posture. For the latter, the article specifies core governance conditions including lawful authority, meaningful human review, explainability, contestability, privacy protection, fairness monitoring, auditability, and redress.The article’s central contribution is to reframe entitlement spending reform as an administrative-capacity challenge alongside a fiscal-policy challenge, and to position agentic AI as a possible governed augmentation layer rather than a vehicle for autonomous administration. It also proposes a multidimensional evaluation framework that assesses not only efficiency or savings, but also legality, burden, fairness, reviewability, accountability, and institutional legitimacy. The article concludes that agentic AI may have a legitimate role in entitlement administration only when embedded within strong governance structures that preserve democratic accountability and protect rights.