Agent Sprawl

Agent sprawl refers to the uncontrolled proliferation of AI agents across an organization, where multiple departments or teams deploy autonomous AI systems without centralized oversight or coordination. This creates governance challenges including duplicated functionality, inconsistent security standards, difficult audit trails, and unpredictable resource consumption that can conflict with enterprise policies. Managing agent sprawl is critical for compliance teams because unmonitored agents may violate data handling requirements, introduce hidden operational risks, and make it nearly impossible to maintain accountability and traceability across the organization.

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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