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What Is Harness Engineering — Designing the Reins for AI Agents In Part 1 of this series, I talked about the decline of prompt engineering. With CLI-based tools on the scene, the value of manually crafting elaborate prompts was fading. But as 2026 unfolded, I realized that what replaced prompt engineering wasn't simply "better tools." Prompt engineering gave way to context engineering, and now context engineering is giving way to an entirely new paradigm: harness engineering . In this post, I'll break down what harness engineering is, why it matters right now, and what its key components look like. A Harness for a Horse, a Harness for an Agent A harness originally refers to the tack fitted onto a horse. Bridle, saddle, stirrups — equipment designed not to suppress the horse's power, but to channel it in the right direction. In AI, the term means exactly the same thing. A harness is the entire external system that controls and directs an AI agent's power...

How AI Coding Changed Completely in 18 Months — Is Prompt Engineering Dead? In late November 2024, I used AI for the first time. Eighteen months later, the changes I've witnessed aren't just about better tools. The entire way of working has transformed — and the pace of that transformation is accelerating. At first, a new paradigm emerged roughly every six months. Then every three months. Now, something new drops almost every week. AI has moved past its infancy and entered a full-blown transition period. Standing in the middle of it, I thought it was worth looking back at these 18 months. The Past — Copy-Paste and Prompt Engineering The Chat Window Era To be precise, it started with asking ChatGPT about code. I'd paste a function or an error message, get a response, copy it, and move it to my editor. The novelty of asking AI instead of searching Google was refreshing, and I was genuinely impressed by the accuracy. But fundamentally, the workflow wasn't all that d...

What Happens When You Hand AI a Scenario Throughout this project, the area where I talked most with AI was scenario design. System architecture, character settings, conflict patterns, time structure. AI was used in every step. I want to leave an honest review. What AI does well, what it cannot do, and how to use it effectively. What AI Does Well Organizing structure. This is overwhelming. Say "I want to design an emotion system," and it immediately proposes a systematic variable structure with layer separation. Information organization that would take a person days is done in minutes. The skeleton of the three-layer emotion model came out within an hour of conversation with AI. Verifying consistency between variables. It answers logically to questions like "If trust goes up in this event, what should happen to tension?" When variables multiply, humans start missing things; AI does not. Presenting patterns. For requests like "Classify the types of confl...