gan-style-harness

Multi-agent iteration improves software quality by separating generation from evaluation, using a dual-agent loop where an Evaluator critiques a Generator's output against configurable rubrics to drive iterative refinement.

A GAN-style harness for autonomous app development orchestrates Planner, Generator, and Evaluator roles to separate generation from evaluation, reducing single-agent optimism and driving higher-quality software outputs through iterative refinement.

Automated evaluation rubrics for end-to-end product prototyping are configured within the harness iteration controls, allowing you to tailor quality goals and evaluation criteria to your specific frontend and backend project context.

Yes, the multi-agent harness supports end-to-end task workflows across both frontend and backend development, handling everything from initial planning and prototyping to live-testing with automated evaluation.

Single-agent generation produces lower quality software outputs due to inherent optimism and lack of adversarial critique, whereas a GAN-style approach uses an Evaluator to continuously challenge and refine the Generator's progress.

Limitations of using a GAN-style evaluation loop include the computational overhead of multiple iterations and the need to explicitly configure evaluation rubrics and iteration controls to prevent endless refinement cycles.