Self-Reflective Agents: Engineering Meta-Cognition in AI for Ethical Autonomous Decision-Making

Abstract

This paper introduces a novel architecture for embedding meta-cognitive capabilities in AI agents, enabling them to audit their own reasoning and assess ethical implications before making autonomous decisions. The proposed framework employs layered transformer models to simulate ethical reflection, validated through real-time scenario simulations in autonomous vehicles and military UAV systems. By integrating ethical rule validators within the transformer architecture, the system facilitates "thought auditing," allowing AI agents to evaluate the morality and safety of potential actions. The effectiveness of this approach is measured using metrics such as self-reflection rate, ethical alignment score, and safety override triggers. The findings suggest significant improvements in ethical decision-making and compliance with safety standards, highlighting the potential for policy implications in AI regulation and defense compliance.