Harness design for long-running application development

This post details the application of a GAN-inspired generator-evaluator architecture to improve AI-driven frontend design and full-stack application development. For frontend design, specific grading criteria (design quality, originality, craft, functionality) were developed and used to train both generator and evaluator agents, driving more distinctive and aesthetically adventurous outputs. For full-stack development, a three-agent system (planner, generator, evaluator) was implemented, leveraging context resets (for Claude Sonnet 4.5) or automatic compaction (for Opus 4.5) to manage long-running autonomous coding sessions and address issues like context anxiety and self-evaluation leniency. The work highlights the effectiveness of separating generation from evaluation for subjective tasks and the importance of harness design for long-running agentic coding.