Software quality metrics provide quantifiable measures for assessing code health, team performance, and system reliability throughout the development lifecycle. These metrics span from operational performance indicators like DORA metrics to structural code properties like complexity and coupling. In an era where AI-assisted code generation is accelerating development, understanding and tracking quality metrics becomes essential—not as vanity numbers, but as actionable signals that identify risk, guide refactoring decisions, and prevent technical debt from becoming technical bankruptcy. The key differentiator between high-performing teams and struggling ones isn't whether they use metrics, but whether they track the right ones and act on the insights they reveal.