Towards Self-Healing Bank IT Systems: The Emergence of Agentic AI in Infrastructure Monitoring and Management
Keywords:
Agentic AI,Self-healing systems,IT infrastructure resilience,Banking IT systems,Autonomous monitoring,AI-driven management,Predictive maintenance,Proactive fault recovery,Distributed systems,Infrastructure automation,AI in finance,Root cause analysis,Adaptive AI agents,Cloud-native monitoring,Legacy system integration.Abstract
Modern banking IT infrastructures are becoming increasingly complex, distributed, and mission-critical, necessitating robust solutions for real-time monitoring, fault detection, and autonomous recovery. This paper explores the transformative potential of agentic artificial intelligence—autonomous, proactive, and adaptive AI agents—in realizing self-healing capabilities within banking IT ecosystems. We examine the limitations of traditional monitoring and management systems and highlight how agentic AI leverages machine learning, systems thinking, and continuous feedback loops to anticipate issues, orchestrate interventions, and ensure operational continuity. Case studies and conceptual frameworks are presented to illustrate the deployment of such agents in dynamic environments, including cloud-native architectures and legacy system integrations. The paper outlines the benefits, challenges, and governance implications of adopting agentic AI, ultimately making the case for a paradigm shift in how financial institutions approach infrastructure resilience.
