Agentic AI Governance and Lifecycle Management in Healthcare

TL;DR

This paper introduces UALM, a comprehensive blueprint for managing agentic AI lifecycle and governance in healthcare to address agent sprawl, accountability, and security challenges.

Contribution

It presents a practice-oriented, layered control-plane framework and maturity model for effective, audit-ready oversight of agentic AI in healthcare settings.

Findings

UALM maps governance gaps onto five control layers.

The framework supports staged adoption with a maturity model.

UALM enables safer scaling and accountability in healthcare AI.

Abstract

Healthcare organizations are beginning to embed agentic AI into routine workflows, including clinical documentation support and early-warning monitoring. As these capabilities diffuse across departments and vendors, health systems face agent sprawl, causing duplicated agents, unclear accountability, inconsistent controls, and tool permissions that persist beyond the original use case. Existing AI governance frameworks emphasize lifecycle risk management but provide limited guidance for the day-to-day operations of agent fleets. We propose a Unified Agent Lifecycle Management (UALM) blueprint derived from a rapid, practice-oriented synthesis of governance standards, agent security literature, and healthcare compliance requirements. UALM maps recurring gaps onto five control-plane layers: (1) an identity and persona registry, (2) orchestration and cross-domain mediation, (3) PHI-bounded context and memory, (4) runtime policy enforcement with kill-switch triggers, and (5) lifecycle management and decommissioning linked to credential revocation and audit logging. A companion maturity model supports staged adoption. UALM offers healthcare CIOs, CISOs, and clinical leaders an implementable pattern for audit-ready oversight that preserves local innovation and enables safer scaling across clinical and administrative domains.