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This arXiv preprint proposes a Three-Pillar (3P) model for governing autonomous AI agents, built around transparency, accountability, and trustworthiness. It describes staged autonomy increases backed by validation, human oversight, and auditable controls. It is a conceptual framework with no reported empirical evaluation, so its practical value depends on how it performs in real enterprise deployments.
A Three-Pillar Governance Framework for Safe and Trustworthy AI Agents
arXiv:2610.30445v1 Announce Type: new Abstract: AI agents capable of autonomous decision-making introduce significant challenges related to safety, alignment, accountability, and human oversight. Existing Human-in-the-Loop (HITL) and responsible AI approaches provide important foundations, but many remain fragmented across system design, governance, and operational deployment. This paper presents a Three-Pillar (3P) model and framework for developing safe and trustworthy AI agents through transparency, accountability, and trustworthiness. The framework defines an evolutionary path in which increasing agent autonomy is introduced through progressive validation, structured human oversight, and auditable operational controls. We further describe how the model supports agent lifecycle monitoring, decision traceability, escalation management, and human-agent collaboration within enterprise agentic environments. By integrating oversight principles with operational mechanisms, the proposed framework provides a practical foundation for deploying AI agents that remain aligned with human intent while enabling controlled progression toward higher autonomy.
Abstract:AI agents capable of autonomous decision-making introduce significant challenges related to safety, alignment, accountability, and human oversight. Existing Human-in-the-Loop (HITL) and responsible AI approaches provide important foundations, but many remain fragmented across system design, governance, and operational deployment. This paper presents a Three-Pillar (3P) model and framework for developing safe and trustworthy AI agents through transparency, accountability, and trustworthiness. The framework defines an evolutionary path in which increasing agent autonomy is introduced through progressive validation, structured human oversight, and auditable operational controls. We further describe how the model supports agent lifecycle monitoring, decision traceability, escalation management, and human-agent collaboration within enterprise agentic environments. By integrating oversight principles with operational mechanisms, the proposed framework provides a practical foundation for deploying AI agents that remain aligned with human intent while enabling controlled progression toward higher autonomy.
Artificial Intelligence (cs.AI)
arXiv:2610.30445 [cs.AI]
(or arXiv:2610.30445v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.30445 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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