The report introduces an analytical framework for assessing risks that arise when AI agents interact, especially as those interactions cross organisational boundaries. It defines three deployment tiers—singular governance, federated governance, and open environments—each with distinct governance requirements. For each tier, the report examines risk factors, failure modes, and controls, identifying who can apply them and highlighting gaps that require collective action.
arXiv:2608. 10153v1 Announce Type: new Abstract: Enterprises are deploying autonomous AI agents faster than they can govern them, and prevailing approaches stretch a single discipline, typically DevSecOps built for deterministic automation, across every scale of agency.
arXiv:2606. 07119v1 Announce Type: cross Abstract: The current phase of enterprise AI deployment faces a structural failure: organisations are acquiring agentic capability without the infrastructure to govern it.
arXiv:2609.21192v1 Announce Type: new Abstract: Organizations deploying agentic artificial intelligence must determine more than whether a model is trustworthy; they must establish what to validate,...
arXiv:2607. 21345v1 Announce Type: new Abstract: Regulating activities where regulatees use autonomous and agentic AI is challenging.
arXiv:2607. 09586v1 Announce Type: new Abstract: The proliferation of agentic AI systems across enterprise and public-sector contexts has outpaced the capacity of general-purpose AI risk frameworks to classify and govern them.
arXiv:2607. 03510v1 Announce Type: cross Abstract: Enterprise artificial intelligence is moving from experimentation into operational workflows.
The paper argues that AI governance should rely on ISO-like interoperability protocols rather than solely on jurisdiction-specific laws. It proposes standardized AI nutrition labels that include metrics for bias, energy usage, and data provenance to enable machine‑readable risk communication across borders. These protocols aim to reduce regulatory fragmentation, lower barriers for SMEs, and build public trust while allowing modular evolution with technology.
The paper proposes AI Deployment Accountability Engineering (ADAE), a new subdiscipline focused on establishing measurable, continuous, and actionable accountability for AI systems once they are deployed. ADAE treats accountability as a deployment-layer property, aiming to ensure systems remain within acceptable risk limits, identify failure contexts, attribute failures across technical and human components, and translate technical failures into downstream consequences. The authors outline a research agenda built around four pillars—structured discovery of context-dependent failure modes, privacy-preserving accountability measurement, system-level risk analysis for agentic AI, and translation of technical failures into operational and institutional risks—to support timely intervention in safety-critical socio-technical environments.
arXiv:2505. 21550v2 Announce Type: replace-cross Abstract: Collaborative agentic AI is projected to transform entire industries by enabling AI-powered agents to autonomously perceive, plan, and act within digital environments.
arXiv:2604. 22789v2 Announce Type: replace-cross Abstract: Organizations deploying AI-enabled Intelligent Transportation Systems face fragmented governance: ISO/IEC~42001 demands a certifiable management system, the EU AI Act imposes binding high-risk obligations from August~2026, and the NIST AI Risk Management Framework structures voluntary practice.
arXiv:2606. 26117v1 Announce Type: cross Abstract: This paper introduces the Governance Inversion Hypothesis (GIH) to explain a growing paradox in artificial intelligence (AI) governance: under conditions of increasing regulatory expansion and technological complexity, organisations may become more formally governed while simultaneously experiencing a decline in operational control over AI systems.
arXiv:2609.06543v1 Announce Type: new Abstract: Enterprise AI is evolving into an Enterprise Operating System where autonomous AI agents can plan, reason, use memory, invoke tools, execute workflows,...