Architecting Agentic AI for IT Operations: Design Principles for Enhanced Automation and Resilience

Abstract

The complexity of modern IT operations, driven by cloud adoption, microservices, and DevOps, challenges traditional management, causing inefficiencies and reactive incident resolution. This paper proposes Agentic AI as a transformative paradigm for truly autonomous IT operations, progressing beyond automation to intelligent and self-governing systems. This paper presents a framework for Agentic AI in IT operations, highlighting key components: Perception, knowledge and memory, decision, and Action, along with the importance of multi-agent orchestration and human-agent collaboration. We outline key design principles for robust autonomous systems, including progressive autonomy, self-healing, observability and explainability, scalability and elasticity, security by design, and continuous learning. Implementation strategies highlight cloud-native approaches and integration with existing IT ecosystems. We acknowledge challenges such as building trust, managing integration complexity, and addressing ethics, while identifying future research directions like human-AI teaming. This paper offers a roadmap for enhancing automation, improving resilience, and optimizing efficiency, enabling organizations to navigate digital transformation with agility.