Large language models often get most of the attention in AI discussions. New releases, benchmark scores, and reasoning capabilities dominate headlines. Yet when companies try to turn AI demos into dependable products, the biggest challenge usually comes from elsewhere. The real difference between an impressive prototype and a production-ready AI system is often the infrastructure surrounding the model. That surrounding layer is known as the AI harness. Many AI projects fail not because the models are weak, but because the systems controlling them are unstable, inconsistent, or impossible to scale safely. As AI agents become more common in software engineering, automation, customer support, and research workflows, harness engineering is quickly becoming one of the most important areas in modern AI development. What Is AI Harness Engineering? A language model alone only generates tokens.…