Code complexity is a significant challenge in software development, often exacerbated by architectural choices, over-engineering, inconsistent practices, and inadequate documentation, leading to increased maintenance efforts, slower delivery, and a higher risk of bugs. Metrics such as cyclomatic complexity, cognitive complexity, Halstead effort, and lines of code are crucial for quantifying complexity, identifying high-risk modules, and informing testing and refactoring decisions. Tools like Qodo offer AI-driven analysis to manage complexity by providing context-aware insights, enforcing coding standards, and suggesting actionable improvements, thereby maintaining modular, readable, and testable code. Qodo integrates with Git to ensure new code aligns with architectural standards, reducing technical debt and preventing complexity from silently accumulating. The article emphasizes the importance of proactive complexity management throughout the software lifecycle to ensure maintainability, scalability, and quality, highlighting real-world examples and the practical application of Qodo's features in a development environment.