A news summary based solely on the provided article body: Harness engineering is presented as the essential operating system for AI software delivery, turning AI agent output into reliable, repeatable, and governable software. A harness combines context, skills, tools, permissions, verification, and feedback to make correct work easier and unsafe work harder. The article states that the durable enterprise advantage will be the harness around the model, not the model alone. DORA’s recent research has highlighted that individual developer productivity gains do not automatically translate into stronger stability, throughput, or delivery outcomes. A harness defines what an agent can discover, which tools it uses, what permissions it receives, which workflows it follows, and what checks it must pass. The five controls that make a harness work are: Context (making the system legible by exposing service boundaries, ownership, APIs, and data contracts in the repository), and Skills (turning expert practice into reusable capability by packaging a procedure, relevant context, approved tools, expected evidence, and quality checks).