AI agent providers face an exhaustive inventory requirement for actions and data flows, as high-risk systems with untraceable behavioral drift cannot meet the AI Act's essential requirements.
Artificial Intelligence Risk Management Framework: Genera- tive Artificial Intelligence Profile
6 Pith papers cite this work. Polarity classification is still indexing.
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Agentic AI evaluation and governance lack mechanisms to bind obligations to actions and prove compliance at runtime; a new synthesis framework with ODTA criteria and action-evidence bundles addresses this closure gap.
Reasoning becomes traceable and governable when relocated to structured human-AI interaction protocols instead of being engineered inside models alone.
The paper presents a layered method to translate governance objectives from standards such as ISO/IEC 42001 into four control layers for agentic AI, with runtime guardrails limited to observable, determinate, and time-sensitive controls, shown via a procurement-agent case study.
AI to Learn 2.0 is a deliverable-oriented framework with a seven-dimension maturity rubric and capability-evidence ladder that permits opaque AI for exploration but requires final outputs to be auditable, transferable, and supported by human-attributable evidence.
citing papers explorer
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AI Agents Under EU Law
AI agent providers face an exhaustive inventory requirement for actions and data flows, as high-risk systems with untraceable behavioral drift cannot meet the AI Act's essential requirements.
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Beyond Task Success: An Evidence-Synthesis Framework for Evaluating, Governing, and Orchestrating Agentic AI
Agentic AI evaluation and governance lack mechanisms to bind obligations to actions and prove compliance at runtime; a new synthesis framework with ODTA criteria and action-evidence bundles addresses this closure gap.
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Governing Reflective Human-AI Collaboration: A Framework for Epistemic Scaffolding and Traceable Reasoning
Reasoning becomes traceable and governable when relocated to structured human-AI interaction protocols instead of being engineered inside models alone.
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From Governance Norms to Enforceable Controls: A Layered Translation Method for Runtime Guardrails in Agentic AI
The paper presents a layered method to translate governance objectives from standards such as ISO/IEC 42001 into four control layers for agentic AI, with runtime guardrails limited to observable, determinate, and time-sensitive controls, shown via a procurement-agent case study.
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AI to Learn 2.0: A Deliverable-Oriented Governance Framework and Maturity Rubric for Opaque AI in Learning-Intensive Domains
AI to Learn 2.0 is a deliverable-oriented framework with a seven-dimension maturity rubric and capability-evidence ladder that permits opaque AI for exploration but requires final outputs to be auditable, transferable, and supported by human-attributable evidence.
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