Agent Lifecycle Management

Agent lifecycle management refers to the processes and controls for overseeing AI agents from initial design and deployment through monitoring, updating, and eventual retirement. For enterprises, this includes tracking agent versions, managing dependencies, controlling access to tools and data, and ensuring compliance with regulations throughout the agent's operational lifetime. Effective lifecycle management is critical for maintaining security, auditability, and control as AI agents become increasingly autonomous in handling business-critical functions.

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ResearchGlobal2026-06-29

Healthcare Agentic AI Faces a Lifecycle Governance Gap: UALM Framework Proposes Five-Layer Architecture and KPI-Linked Thresholds

A peer-reviewed paper from the Healthcare Research Consortium introduces the Unified Agent Lifecycle Management (UALM) framework, a five-layer governance architecture and accompanying maturity model designed specifically for agentic AI in healthcare settings. The framework addresses documented gaps in existing standards that were not built to handle distributed autonomy across interacting agents. Using Monte Carlo simulation, the authors quantify operational behavior under alternative governance assumptions, providing empirical grounding for control design.

agentic AI healthcare AI governance agent lifecycle management multi-agent systems clinical AI compliance