Multi-Agent AI

Multi-agent AI refers to systems where multiple autonomous or semi-autonomous AI agents collaborate, compete, or operate independently to accomplish tasks or goals. In enterprise governance and compliance contexts, multi-agent AI introduces heightened complexity around accountability, coordination, and risk management, as it becomes harder to predict emergent behaviors and trace decisions back to individual agents. Organizations must establish frameworks for monitoring agent interactions, managing conflicts between competing objectives, and ensuring that the collective system behavior remains aligned with regulatory requirements and organizational policies.

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ResearchGlobal2026-07-23

Healthcare Multi-Agent AI Creates Ownership and Retirement Gaps That Standard Governance Frameworks Do Not Cover, arXiv Research Warns

A preprint published on arXiv proposes a comprehensive governance and lifecycle management framework for agentic AI systems deployed in healthcare settings. The framework focuses on the specific risks that arise when autonomy is distributed across multiple interacting agents, including unclear ownership, uncontrolled permission expansion, and the absence of structured retirement processes. Its core argument is that enterprise-level visibility and enforceability are prerequisites for safe deployment of these systems.

multi-agent AI healthcare AI lifecycle management agentic AI agent sprawl ownership model retirement