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Agentic AI Governance and Lifecycle Management in Healthcare

Chandra Prakash, Mary Lind, Avneesh Sisodia

TL;DR

The paper tackles agent sprawl in healthcare by treating autonomous, goal-directed agents as a governance and lifecycle problem. It introduces the Unified Agent Lifecycle Management (UALM) framework, a five-layer control-plane augmented by a maturity model and KPI set to govern identity, orchestration, memory, enforcement, and decommissioning. The method translates risk frameworks into an auditable, runtime-enforceable architecture tailored to HIPAA and healthcare operations. The proposed pattern enables audit-ready oversight, scalable deployment across domains, and preserved domain autonomy for CIOs, CISOs, and clinical leaders.

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.

Agentic AI Governance and Lifecycle Management in Healthcare

TL;DR

The paper tackles agent sprawl in healthcare by treating autonomous, goal-directed agents as a governance and lifecycle problem. It introduces the Unified Agent Lifecycle Management (UALM) framework, a five-layer control-plane augmented by a maturity model and KPI set to govern identity, orchestration, memory, enforcement, and decommissioning. The method translates risk frameworks into an auditable, runtime-enforceable architecture tailored to HIPAA and healthcare operations. The proposed pattern enables audit-ready oversight, scalable deployment across domains, and preserved domain autonomy for CIOs, CISOs, and clinical leaders.

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.
Paper Structure (11 sections, 3 figures)

This paper contains 11 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Background
  3. Prior Work
  4. The Need for Agentic AI Governance and Lifecycle Management
  5. Research Questions
  6. Methodology
  7. The Unified Agent Lifecycle Management (UALM) Framework: A 5-Layer Framework for Governing Agentic AI Sprawl in Healthcare
  8. Maturity Model
  9. An Adoption-Centred Governance Framework
  10. KPIs and Metrics
  11. Discussion and Conclusion

Figures (3)

  • Figure 1: Visualization of Agentic AI Maturity Model
  • Figure 2: Agentic AI Governance and Lifecycle Management Framework
  • Figure 3: The Unified Agent Lifecycle Management (UALM) Reference Architecture.