In safety-critical industries, detecting anomalies is only part of the challenge; understanding their underlying causes is equally critical for effective decision-making and system reliability. Traditional anomaly detection methods focus solely on identifying anomalies without offering explanations, leaving a significant gap in actionable insights. Additionally, these methods rely heavily on processing raw data, which is often vast, complex, and resource-intensive to handle. As a result, the burden of interpreting detected anomalies falls on human experts, who must verify the findings and analyze potential root causes. This manual process not only incurs high operational costs but also introduces delays that could be critical in time-sensitive scenarios. Addressing these limitations requires innovative approaches that integrate anomaly detection with automated reasoning and diagnostics. Such approaches must prioritize scalability and adaptability, ensuring seamless integration into existing workflows without compromising performance. To address these limitations, the primary goal of this work was to develop a plug-and-play automated pipeline for anomaly detection and reasoning. The proposed solution eliminates the need for additional model training, making it versatile and suitable for general-purpose applications across various domains.