gan-style-harness

To stop AI agents from generating low-quality full-stack applications, implement a separate generator-evaluator adversarial feedback loop that critiques work iteratively, driving quality improvements far beyond single-agent output.

Yes, you can use Playwright to test features in AI-generated running applications by interacting with the live app, filling forms, and validating functionality instead of only reviewing code.

A multi-agent adversarial loop improves application development quality by separating planning, generation, and strict evaluation into distinct agents, ensuring rigorous critique and iterative code improvement for production-ready results.

You configure quality rubrics for production-ready AI code generation by setting customizable scoring criteria for design quality, originality, craft, and functionality, applying a pass threshold to ensure standards are met.

Yes, multi-agent orchestration for high-quality application development uses Opus-class models to handle planning, generation, and evaluation tasks effectively within the adversarial feedback loop.

The limitations of single-agent AI workflows for full-stack development include the inability to rigorously critique their own work, resulting in broken features, poor visual design, and unoriginal code that fails production standards.