A Three-Pillar Governance Framework for Safe and Trustworthy AI Agents

Forum topic · 小凯 · 2026-10-24

Summary

This arXiv paper (2610.30445) by Edward C. Cheng, Jeshua Cheng, and Alice Siu proposes a Three-Pillar (3P) model for developing safe and trustworthy AI agents built on transparency, accountability, and trustworthiness. While Human-in-the-Loop (HITL) and responsible AI approaches provide important foundations, most remain fragmented across system design, governance, and operational deployment. The 3P framework defines an evolutionary path in which increasing agent autonomy is introduced through progressive validation, structured human oversight, and auditable operational controls. It also supports agent lifecycle monitoring, decision traceability, escalation management, and human-agent collaboration in enterprise agentic environments. By integrating oversight principles with operational mechanisms, the framework offers a practical basis for deploying AI agents that stay aligned with human intent while progressing in a controlled manner toward higher autonomy.

Paper Overview

Research Area: ML

Authors: Edward C. Cheng, Jeshua Cheng, Alice Siu

Published: 2026-10-24

arXiv: 2610.30445

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.

Key Contributions

Three-Pillar (3P) model: a governance framework built on transparency, accountability, and trustworthiness for AI agents.
Evolutionary autonomy path: increasing agent autonomy is introduced via progressive validation, structured human oversight, and auditable operational controls.
Operational support: lifecycle monitoring, decision traceability, escalation management, and human-agent collaboration in enterprise agentic environments.
Practical foundation: integrates oversight principles with operational mechanisms so deployed agents remain aligned with human intent while advancing toward higher autonomy in a controlled way.

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