The Multi‑Agent Event Intelligence Solution (EIS) addresses one of the most urgent challenges facing CSPs today: operating increasingly distributed, cloud‑native, and fast‑changing networks with legacy O&M approaches that were never designed for this level of scale or dynamism. As operators confront overwhelming volumes of alarms, fragmented telemetry, and reactive incident workflows, they struggle to maintain service reliability, protect revenue, and deliver consistent customer experience. These pressures not only raise operational cost but also hinder the industry's ability to progress toward autonomous network operations. EIS reimagines network operations through a coordinated system of goal‑driven AI agents that collaborate across the full incident lifecycle. Each agent specializes in a core operational function—including incident narrative generation, lifecycle management, anomaly interpretation, RCA, prediction, and remediation—while sharing context, memory, and knowledge graphs to produce real‑time situational awareness. This agentic architecture enables intelligent, continuous sensing and closed‑loop decisioning that moves CSPs beyond isolated analytics and static automation toward proactive, adaptive, and autonomous network behaviors. The business impact is both immediate and measurable. By reducing noise, correlating events in real time, and accelerating both detection and resolution, EIS delivers tangible improvements: 30–60% faster MTTD, 40–70% faster MTTR, and 40–80% fewer false positives. Its ability to classify, correlate, and remediate incidents autonomously lays the foundation for L3/L4 autonomous operations, allowing CSPs to scale without proportional workforce growth. Improved SLA compliance, reduced OPEX, and enhanced service availability translate into a stronger competitive position and a demonstrably better end‑user experience. Built on an extensible, future‑ready AI architecture, the Catalyst showcases how agentic intelligence can integrate seamlessly with existing OSS/BSS ecosystems while maintaining transparent, human‑in‑the‑loop governance essential for mission‑critical environments. With components such as LLM‑driven reasoning, MCP‑based tool orchestration, and shared knowledge models already validated in early deployments, EIS provides a practical and scalable blueprint for the industry’s transition toward intent‑based, autonomous networks. This Catalyst demonstrates not only innovation, but a clear path for operators to achieve transformative, autonomous O&M at scale.