TAgentic AIOps for Resilient Enterprise Operations: A Closed-Loop, Evidence-Aware Architecture for Incident Triage, RCA, and SLO Governance

Abhradeep Chatterjee, NTT DATA Services, United States

ABSTRACT

Modern enterprise operations face a compounding failure surface created by microservices sprawl, hybrid cloud dependencies, and continuous delivery. Traditional AIOps pipelines detect anomalies but often stop short of trustworthy, auditable actions, leaving the highest-cost minutes of an incident—triage, correlation, and root-cause analysis—largely manual. This paper presents an agentic, closed-loop AIOps architecture that couples event intelligence with evidence-aware reasoning, policy-guarded action execution, and continuous learning from outcomes. The design unifies multi-source telemetry ingestion, causal-graph correlation, retrieval-augmented runbook planning, risk-scored remediation with human-in-the-loop controls, and SLO-governed feedback. We define an evaluation protocol spanning detection quality, diagnostic latency, action safety, and operator load, and provide a simulation harness to compare alerting, classic AIOps, and agentic AIOps. Simulation results across three scenarios show improved triage and mitigation speed while keeping unsafe actions near-zero via policy gating.

KEYWORDS

AIOps, agentic systems, incident management, root cause analysis, SLO governance.

Comparative Performance Analysis of Synthetic Minority Oversampling Techniques (Smote) on Medical Datasets Based on Extreme Gradient Boosting Estimator

Yusuf Abubakar Kutigi, Abdullahi Muhammad Bashir, Mohammed Abdulmalik Danlami and Adabara Nasiru Usman, 1University of Maiduguri, Maiduguri, Nigeria, 2,3,4Federal University of Technology Minna

ABSTRACT

The medical datasets are often confronted with the problems of class imbalance and redundancy of the features, which could affect the quality of classification prediction. In this study, the performance of four approaches to the Synthetic Minority Oversampling Technique (SMOTE)—SMOTE-ENN, Borderline SMOTE, ADASYN, and SMOTE-Tomek Links—has been studied together with feature selection and the XGBoost classifier in order to predict breast cancer and heart disease. The used datasets were processed to exclude noises, to make the distribution even, and to select the most important features. The analysis of the model has been conducted based on several criteria, such as accuracy, precision, recall, F1-score, and Cohens Kappa. For the heart disease data, the best results were obtained for the SMOTE-ENN approach, with accuracy, recall, and F1-score equal to 56.15%, 44.50%, and 27.46% correspondingly, that allowed detecting minority class cases. At the same time, for the breast cancer data, all other approaches provided better results, including accuracy, recall, F1-score, and Kappa of 96.49%, 100%, 97.30%, and 92.31% respectively.

KEYWORDS

SMOTE, feature selection, XGBoost, class imbalance, breast cancer, heart disease, machine learning, medical diagnosis.