This analysis reveals a critical tension in AI-assisted engineering: the gap between theoretical capability and real-world reliability. The study’s methodology—automated testing with categorized prompts—provides a robust framework for evaluating AI agent performance, but its findings also expose deeper systemic challenges. The reliance on "aggressive language" or verbose prompts to improve invocation rates suggests that current AI models still struggle with nuanced intent recognition, a limitati...