The Production Blind Spot: Why AI-Generated Code Fails in the Real World

As AI coding assistants gain adoption, a critical vulnerability emerges: code that passes tests but fails under real-world conditions. The analysis reveals that LLMs exhibit consistent, preventable mistakes—particularly in async/await handling, error management, and null-safety—that traditional linters overlook. A purpose-built ESLint plugin targeting AI-specific code patterns could significantly improve production reliability and enable teams to safely scale AI-assisted development.