Time-series anomaly detection has traditionally been dominated by statistical methods and narrow Deep Learning architectures. Most well-known algorithms require extensive feature engineering and struggle with complex signals; furthermore, while certain neural models show better performance in pattern recognition, they remain "black boxes" that lack explainability and the ability to incorporate domain context. Often, these systems fall victim to an "illusion of progress" where high numerical scores mask a lack of true semantic understanding of actual system behavior. In contrast, the emergence of Foundational Models introduces a shift towards generalized intelligence, culminating in the development of Vision-Language Models (VLMs). Unlike previous deep learning models, VLMs are natively multimodal, enabling the transformation of time-series data into visual representations.