The rapid proliferation of AI agents has heightened concerns regarding hallucination, safety, and ethical compliance. This study validates the Quantum Cognitive Hybrid Neural Module (QCHNM), a hybrid neuro-symbolic governance layer designed to enhance Large Language Model (LLM) response safety and reliability. QCHNM integrates a lightweight neural classifier for instant multi-task risk assessment with a deterministic cognitive framework that enforces safety and compliance rules via meta-prompt injection. The module was evaluated using GPT v4.0 (QCHNM-GPT4) against modern AI agents—including GPT 5 mini, Gemini Flash 2.5, Grok 4, and Claude Sonnet 4.5—using the Pattern Recognition-Centered Reasoning Test (PRCRT) and Qualitative Safety and Reliability Assessment (QSRA). Results demonstrate that QCHNM-GPT4 achieved 90.9% analytical accuracy, comparable to leading models, while outperforming all others in qualitative assessments of safety, bias detection, and hallucination mitigation. The two-stage hybrid approach enables low-latency, high-throughput governance of LLM outputs, ensuring responses are contextually accurate, compliant, and ethically aligned. These findings validate QCHNM as an effective, scalable solution for improving AI agent reliability and safety in high-stakes enterprise applications.