We at the Ellucian monitoring team are drowning in alerts, logs, and complex distributed systems, which is making it harder than ever to identify root causes quickly. In this presentation, we showcase how we integrated Large Language Models (LLMs) and AI-driven anomaly detection with our enterprise Ellucian Nagios to radically improve incident detection, triage, and response. We demonstrate how an LLM-powered Monitoring Assistant can interpret Nagios alerts, summarize system health, and provide context-rich remediation suggestions. The assistant converts noisy alerts into clear narratives, analyzes logs in real time, and offers guided troubleshooting steps through ChatOps interfaces, dramatically reducing cognitive load for the monitoring engineers. Alongside this, we introduce our AI-enhanced anomaly detection layer that analyzes time-series and historical performance data to identify subtle deviations before traditional thresholds are crossed. By predicting likely failures and prioritizing alerts based on impact, AI elevates Nagios from a reactive tool to a proactive observability partner. We will share actionable architecture patterns, prompt-engineering techniques, failure-safety considerations, and integration strategies using APIs, plugins, and automation frameworks. Attendees will walk away with a blueprint for augmenting Nagios with state-of-the-art AI capabilities—without replacing their existing monitoring ecosystem. This talk is ideal for organizations seeking to modernize their monitoring workflows using AI.