AI-Driven Autonomous IT Operations: A Human-in-the-Loop AIOps 2.0 Framework
Keywords:
AIOps, Human-in-the-Loop, Autonomous IT Operations, Intelligent Incident Management, Predictive AnalyticsAbstract
Artificial Intelligence for IT Operations (AIOps) has become a game-changer for the management of large and complex digital environments, driven by intelligent automation, predictive analytics, and decision-making. Current AIOps solutions, however, face challenges with explainability, governance, interoperability, and rely too much on fully automated remediation. This paper introduces a new framework for Human-in-the-Loop AIOps 2.0, combining anomaly detection with reinforcement learning and human validation, to ensure reliable autonomous IT operations. The proposed methodology involves utilising the Kaggle IT Incident Log Dataset, Data Preprocessing, Feature Engineering, anomaly detection using Isolation Forest (IF) algorithm and a Deep Q-Network (DQN)-based recommendation engine to create intelligent remediation actions. Human operators review AI-generated decisions for validation before execution, enhancing the trust, transparency, and governance of the operation. Through experimental assessment, the proposed framework has been proven to be the most effective with an accuracy (ACC) of 94.6%, precision (PRE) of 94.1%, recall (REC) of 94.8%, and F1 Score (F1) of 94.4% better than traditional monitoring systems and fully AI-driven monitoring systems. Additionally, the human validation process can greatly minimize false positives and mean time to resolution, and enhance downtime reduction. The findings of the study underline the need to build autonomous intelligence and integrate it with human intelligence for next-generation resilient, explainable and adaptive IT operations management.
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