gan-style-harness

Adversarial generation-and-evaluation improves full-stack application scaffolding by separating code generation from strict rubric-based critique, iterating until UI cohesion and functionality pass weighted thresholds. This prevents low-quality AI slop by relentlessly checking the running application in real conditions.

Playwright testing evaluates generated UI-heavy applications by driving live browser interactions against the running app rather than judging code alone. The evaluator tests real end-to-end UI QA conditions, applying rubric scoring to verify actual user flows and core functionality.

You can turn a one-line prompt into a structured spec using a planner-to-sprints architecture. The harness expands a simple prompt into structured specifications, then iterates through a generation-evaluation loop against explicit criteria to build production-ready applications.

The best way to stop unreliable self-critique from degrading multi-agent app generation is separating the evaluator from the generator. An independent evaluator applies strict rubric-based scoring and live Playwright interaction, controlling iteration with configurable thresholds and maximum cycles.

The generation-evaluation loop supports configurable thresholds and maximum iteration cycles. It controls the adversarial feedback process by setting explicit rubric scoring thresholds and cycle limits, ensuring the generator stops when quality criteria are met or cycles are exhausted.

Your AI-generated application has poor UI cohesion and core functionality because standard generation lacks real-time live evaluation. Without an adversarial loop applying Playwright-driven browser tests and weighted rubric scoring, self-critique remains unreliable for iterative improvement.