A Practical Approach to Defining a Framework for Developing an Agentic AIOps System

The increasing complexity of IT operations necessitates advanced automation to ensure system availability, resilience, continuity, performance, security, and maintainability. Traditional IT management frameworks, such as the Information Technology Infrastructure Library (ITIL), have standardized service management processes, while models like IBM’s Process Reference Model for IT (PRM-IT) have facilitated automation through structured workflows. However, critical tasks, such as incident resolution and problem management, still require significant human intervention. The adoption of development, security, and operations (DevSecOps) introduced standardized remediation playbooks, further enhancing automation. More recently, Generative AI (GenAI) has expanded automation possibilities, opening new avenues for hyper-automated IT operations. This study explored Agentic Artificial Intelligence for IT Operations (Agentic AIOps), an approach that integrates AI-driven agents to autonomously manage IT operations. Our methodology features a framework for designing and developing an Agentic AIOps system that proactively detects anomalies, classifies incidents, and autonomously executes resolution workflows across infrastructure, middleware, data, and applications in complex, heterogeneous Enterprise IT environments. Our findings suggest that such a system can significantly reduce incident response time, enhance predictive maintenance, and enable self-healing IT environments. By leveraging AI agents for real-time decision making, this approach enhances operational efficiency and reduces human workload. We conclude that this Agentic AIOps system represents a transformative step toward fully autonomous IT operations. We outline a framework for defining its architecture and planning its subsequent development, ensuring continuous evolution and adaptability in complex IT landscapes.