Baselines appear in CloudNetX because you cannot identify anomalies or prove improvement without knowing what normal looks like, and many scenario questions depend on that operational reality. This episode defines a baseline as a documented set of normal measurements captured during stable conditions, then explains that baselines can apply to performance, capacity, error rates, and user experience. The first paragraph focuses on why baselines matter: they support troubleshooting by distinguishing real degradation from normal variation, they support planning by revealing growth trends before exhaustion occurs, and they support governance by providing objective evidence during change validation. It explains that baseline selection should align with critical services and flows, including metrics like latency, packet loss, jitter, throughput, utilization, and authentication failure rates, because these are common drivers of incidents in hybrid environments.