Observability metrics are a crucial aspect of monitoring complex systems, enabling the evaluation of a system's internal state based on its external outputs. The concept of observability was introduced by Rudolf Kálmán in 1960 and gained importance with the advent of complex, distributed software systems. Observability involves pairing complex sets of metrics together to analyze them dynamically and identify problems that may not be anticipated in advance. By collecting and correlating various metrics from across a system, it becomes possible to infer what's happening deep within the system, much like inferring the internal state of the Trojan Horse by analyzing its surface-level data. Key observability metrics include infrastructure metrics such as CPU utilization and memory usage, as well as application performance metrics like request rate, latency, error rate, and changes in these metrics over time. Leveraging observability metrics can help manage system performance, identify root causes of issues, provide early alerts for emerging problems, and minimize Mean Time to Remediate (MTTR). Various monitoring and visualization tools are available, including Prometheus and Grafana, which can be used to collect, analyze, and visualize observability metrics. By adopting an effective observability strategy, it becomes possible to make sense of even the most complex systems in a relatively short amount of time.