Automating network management with Gen AI Ops and MongoDB enables operations teams to respond quickly and intelligently to unexpected traffic spikes, reducing the time spent on data cleanup, correlation, and interpretation. By combining MongoDB's developer data platform with large language models (LLMs) and a retrieval-augmented generation (RAG) architecture, organizations can move from reactive "firefighting" to proactive, data-informed diagnostics. The system automatically ingests log entries and telemetry events in real-time, captures textual content, and stores it in MongoDB for semantic search, enabling near-instant access to relevant information whenever a keyword is mentioned. This setup enables teams to pose natural-language questions to the system, generate custom MongoDB Aggregation Pipelines, and receive summarized explanations that point to root causes of issues, such as overloaded local CDN nodes or misbehaving older routers. With this approach, organizations can reduce costs, improve user satisfaction, and extend their AI-driven operations across the entire organization.