Modern medium-voltage (MV) distribution networks face increasing reliability challenges driven by aging assets, climate variability, and evolving operational demands. In Colombia and across Latin America, reliability metrics, such as the System Average Interruption Frequency Index (SAIFI), standardized under IEEE 1366, serve as key indicators for regulatory compliance and service quality. However, existing analytical approaches struggle to jointly deliver predictive accuracy, interpretability, and traceability required for regulated environments. Here, we introduce CRITAIR (Criticality Analysis through Interpretable Artificial Intelligence-based Recommendations), an integrated framework that combines predictive modeling, explainable analytics, and regulation-aware reasoning to enhance reliability management in MV networks. CRITAIR unifies three components: (i) a TabNet-based predictive module that estimates SAIFI using outage, asset, and meteorological data while producing global and local attributions; (ii) an agentic retrieval-and-reasoning stage that grounds recommendations in regulatory evidence from RETIE and NTC 2050; and (iii) interpretable reasoning graphs that map decision pathways. Evaluations conducted on real operational data demonstrate that CRITAIR achieves competitive predictive performance—comparable to Random Forest and XGBoost—while maintaining transparency through sparse attention and sequential feature explainability. Also, our regulation-aware reasoning module exhibits coherent and verifiable recommendations, achieving high semantic alignment scores (BERTScore) and expert-rated interpretability. Overall, CRITAIR bridges the gap between predictive analytics and regulatory governance, offering a transparent, auditable, and deployment-ready solution for digital transformation in electric distribution systems.

Modern medium-voltage (MV) distribution networks face increasing reliability challenges driven by aging assets, climate variability, and evolving operational demands. In Colombia and across Latin America, reliability metrics such as the system average interruption frequency index (SAIFI), standardized under IEEE 1366, serve as key indicator for regulatory compliance and service quality. However, existing analytical approaches struggle to jointly deliver predictive accuracy, interpretability, and traceability required for regulated environments. Here, we introduces CRITAIR (Criticality Analysis through Interpretable Artificial Intelligence-based Recommendations), an integrated framework that combines predictive modeling, explainable analytics, and regulation-aware reasoning to enhance reliability management in MV networks. CRITAIR unifies three components: (i) a TabNet-based predictive module that estimates SAIFI using outage, asset, and meteorological data while producing global and local attributions; (ii) an agentic retrieval-and-reasoning layer that grounds recommendations in regulatory evidence from RETIE and NTC 2050; and (iii) interpretable reasoning graphs that map decision pathways for full auditability. Evaluations conducted on real operational data demonstrate that CRITAIR achieves competitive predictive performance—comparable to Random Forest and XGBoost—while maintaining transparency through sparse attention and sequential feature explainability. Also, our regulation-aware reasoning module exhibits coherent and verifiable recommendations, achieving high semantic alignment scores (BERTScore) and expert-rated interpretability. Overall, CRITAIR bridges the gap between predictive analytics and regulatory governance, offering a transparent, auditable, and deployment-ready solution for digital transformation in electric distribution systems.