This article explores how **multi-agent harness design** significantly enhances the performance of AI models in complex, long-running tasks like **frontend design** and **autonomous software engineering**. The author details a shift from single-agent attempts to a **GAN-inspired architecture** involving specialized **planner, generator, and evaluator** roles to overcome issues like "context anxiety" and poor self-assessment. By implementing **objective grading criteria** and automated testing via tools like Playwright, the system can autonomously iterate on projects for several hours to produce high-fidelity, functional applications. Comparative experiments demonstrate that while these structured harnesses increase **token costs and latency**, they deliver a level of **creative polish and technical correctness** that solo models cannot currently achieve. Ultimately, the work suggests that as underlying models improve, the role of the AI engineer shifts toward refining these **agentic orchestrations** to push the boundaries of what autonomous systems can build.