# Agentic AI Governance and Lifecycle Management in Healthcare

arXiv: https://arxiv.org/abs/2601.15630 · HTML: https://mlsanity.com/paper/2601.15630/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 22 Jan 2026.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 10 of 10)

- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Alignment faking in large language models](https://mlsanity.com/paper/2412.14093/) (2024)
- [SAGA: A Security Architecture for Governing AI Agentic Systems](https://mlsanity.com/paper/2504.21034/) (2025)
- [Characterizing AI Agents for Alignment and Governance](https://mlsanity.com/paper/2504.21848/) (2025)
- [Superhuman performance of a large language model on the reasoning tasks of a physician](https://mlsanity.com/paper/2412.10849/) (2024)
- [Beyond DNS: Unlocking the Internet of AI Agents via the NANDA Index and Verified AgentFacts](https://mlsanity.com/paper/2507.14263/) (2025)
- [Decentralized Governance of Autonomous AI Agents](https://mlsanity.com/paper/2412.17114/) (2024)
- [Recent Advances in Generative AI for Healthcare Applications](https://mlsanity.com/paper/2310.00795/) (2023)
- [The AGNTCY Agent Directory Service: Architecture and Implementation](https://mlsanity.com/paper/2509.18787/) (2025)
- [Agentic AI in 6G Software Businesses: A Layered Maturity Model](https://mlsanity.com/paper/2508.03393/) (2025)

## Abstract (arXiv)

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.