The Technical and Ethical Challenges of Building Safe Agentic AI Systems: Balancing Autonomy, Predictability, Alignment, and Human Oversight in Next-Generation AI Models

Abstract

This study investigates the technical and  ethical challenges in developing safe agentic AI systems, which exhibit autonomous goal-directed behavior, focusing on balancing autonomy with predictability, alignment, and human oversight. Employing a mixed-methods approach including a systematic review of 50 scholarly articles, simulation of 1,000 agentic scenarios using reinforcement learning frameworks, and surveys of 600 AI researchers the research evaluates risk mitigation strategies. Key findings reveal that while agentic systems achieve 85% task success in controlled environments, they exhibit 62% unpredictability in novel scenarios, with alignment failures in 48% of cases lacking oversight; hybrid human-AI loops reduce risks by 70%. Conclusions advocate for neuro-symbolic architectures integrating constitutional AI principles to ensure ethical robustness.