Developers working on AI Agent deployment have likely encountered this frustrating dilemma: using flagship models, revising prompts hundreds of times, tuning RAG systems repeatedly—yet task success rates remain stubbornly low in real-world scenarios, with performance fluctuating unpredictably between brilliant and completely off-track.The root problem lies not in the model itself, but in the operational system surrounding it—the Harness.Understanding Harness EngineeringThe term "Harness" originally refers to reins or restraint devices. In AI...