Balancing autonomy and oversight in reliable agentic artificial intelligence through adaptive human interaction architectures

The transition from Generative AI to Agentic AI has enabled systems to execute complex, multi-step workflows autonomously. However, deploying these autonomous agents in high-stakes environments—such as financial operations, cyber- security, and healthcare—remains risky due to hallucinations, goal misalignment, and cascading errors. Traditional Human- in-the-Loop (HITL) systems often rely on static checkpoints, creating bottlenecks that negate the efficiency gains of autonomy. This paper proposes a novel Dynamic Intervention Framework (DIF) for Agentic AI. The objective is to decouple human oversight from routine agent actions, allowing the system to request human input only when specific uncertainty thresholds or semantic drifts are detected. We developed a multi-agent ar- chitecture utilizing a ”Supervisor-Worker” topology, introducing a Contextual Confidence Score (Ccs) metric to evaluate output probability and semantic alignment. Evaluation on a dataset of 5,000 enterprise automation tasks shows the DIF reduced human workload by 65systems while maintaining a success rate of 98.2that rigid HITL models are insufficient for modern Agentic AI and that autonomy must be paired with adaptive, confidencebased oversight. Index Terms—Digital Forensics, Zero Trust Architecture, MultiAgent Systems (MAS), Large Language Models (LLMs), Forensic Readiness, Explainable AI (XAI), Adversarial Robust- ness.