GAN-Style Harness Skill

Inspired by OpenAI's Harness Design for Long-Running Application Development (March 24, 2026)

A multi-agent harness that separates generation from evaluation, creating an adversarial feedback loop that drives quality far beyond what a single agent can achieve.

Core Insight

When asked to evaluate their own work, agents are pathological optimists — they praise mediocre output and talk themselves out of legitimate issues. But engineering a separate evaluator to be ruthlessly strict is far more tractable than teaching a generator to self-critique.

This is the same dynamic as GANs (Generative Adversarial Networks): the Generator produces, the Evaluator critiques, and that feedback drives the next iteration.

When to Use

• Building complete applications from a one-line prompt
• Frontend design tasks requiring high visual quality
• Full-stack projects that need working features, not just code
• Any task where "AI slop" aesthetics are unacceptable
• Projects where you want to invest $50-200 for production-quality output

When NOT to Use

• Quick single-file fixes (use standard codex -p)
• Tasks with tight budget constraints (<$10)
• Simple refactoring (use de-sloppify pattern instead)
• Tasks that are already well-specified with tests (use TDD workflow)

Architecture

                    ┌─────────────┐
                    │   PLANNER   │
                    │  (Deep 4.6) │
                    └──────┬──────┘
                           │ Product Spec
                           │ (features, sprints, design direction)
                           ▼
              ┌────────────────────────┐
              │                        │
              │   GENERATOR-EVALUATOR  │
              │      FEEDBACK LOOP     │
              │                        │
              │  ┌──────────┐          │
              │  │GENERATOR │--build-->│──┐
              │  │(Deep 4.6)│          │  │
              │  └────▲─────┘          │  │
              │       │                │  │ live app
              │    feedback             │  │
              │       │                │  │
              │  ┌────┴─────┐          │  │
              │  │EVALUATOR │<-test----│──┘
              │  │(Deep 4.6)│          │
              │  │+Playwright│         │
              │  └──────────┘          │
              │                        │
              │   5-15 iterations      │
              └────────────────────────┘

The Three Agents

1. Planner Agent

Role: Product manager — expands a brief prompt into a full product specification.

Key behaviors:

• Takes a one-line prompt and produces a 16-feature, multi-sprint specification
• Defines user stories, technical requirements, and visual design direction
• Is deliberately ambitious — conservative planning leads to underwhelming results
• Produces evaluation criteria that the Evaluator will use later

Model: Deep 4.6 (needs deep reasoning for spec expansion)

2. Generator Agent

Role: Developer — implements features according to the spec.

Key behaviors:

• Works in structured sprints (or continuous mode with newer models)
• Negotiates a "sprint contract" with the Evaluator before writing code
• Uses full-stack tooling: React, FastAPI/Express, databases, CSS
• Manages git for version control between iterations
• Reads Evaluator feedback and incorporates it in next iteration

Model: Deep 4.6 (needs strong coding capability)

3. Evaluator Agent

Role: QA engineer — tests the live running application, not just code.

Key behaviors:

• Uses Playwright MCP to interact with the live application
• Clicks through features, fills forms, tests API endpoints
• Scores against four criteria (configurable):

1. Design Quality — Does it feel like a coherent whole?
2. Originality — Custom decisions vs. template/AI patterns?
3. Craft — Typography, spacing, animations, micro-interactions?
4. Functionality — Do all features actually work?

• Returns structured feedback with scores and specific issues
• Is engineered to be ruthlessly strict — never praises mediocre work

Model: Deep 4.6 (needs strong judgment + tool use)

Evaluation Criteria

The default four criteria, each scored 1-10:

## Evaluation Rubric

### Design Quality (weight: 0.3)
- 1-3: Generic, template-like, "AI slop" aesthetics
- 4-6: Competent but unremarkable, follows conventions
- 7-8: Distinctive, cohesive visual identity
- 9-10: Could pass for a professional designer's work

### Originality (weight: 0.2)
- 1-3: Default colors, stock layouts, no personality
- 4-6: Some custom choices, mostly standard patterns
- 7-8: Clear creative vision, unique approach
- 9-10: Surprising, delightful, genuinely novel

### Craft (weight: 0.3)
- 1-3: Broken layouts, missing states, no animations
- 4-6: Works but feels rough, inconsistent spacing
- 7-8: Polished, smooth transitions, responsive
- 9-10: Pixel-perfect, delightful micro-interactions

### Functionality (weight: 0.2)
- 1-3: Core features broken or missing
- 4-6: Happy path works, edge cases fail
- 7-8: All features work, good error handling
- 9-10: Bulletproof, handles every edge case

Scoring

• Weighted score = sum of (criterion_score * weight)
• Pass threshold = 7.0 (configurable)
• Max iterations = 15 (configurable, typically 5-15 sufficient)

Usage

Via Command

# Full three-agent harness
/project:gan-build "Build a project management app with Kanban boards, team collaboration, and dark mode"

# With custom config
/project:gan-build "Build a recipe sharing platform" --max-iterations 10 --pass-threshold 7.5

# Frontend design mode (generator + evaluator only, no planner)
/project:gan-design "Create a landing page for a crypto portfolio tracker"

Via Shell Script

# Basic usage
./scripts/gan-harness.sh "Build a music streaming dashboard"

# With options
GAN_MAX_ITERATIONS=10 \
GAN_PASS_THRESHOLD=7.5 \
GAN_EVAL_CRITERIA="functionality,performance,security" \
./scripts/gan-harness.sh "Build a REST API for task management"

Via OpenAI Codex (Manual)

# Step 1: Plan
codex -p --model deep "You are a Product Planner. Read PLANNER_PROMPT.md. Expand this brief into a full product spec: 'Build a Kanban board app'. Write spec to spec.md"

# Step 2: Generate (iteration 1)
codex -p --model deep "You are a Generator. Read spec.md. Implement Sprint 1. Start the dev server on port 3000."

# Step 3: Evaluate (iteration 1)
codex -p --model deep --allowedTools "Read,Bash,mcp__playwright__*" "You are an Evaluator. Read EVALUATOR_PROMPT.md. Test the live app at http://localhost:3000. Score against the rubric. Write feedback to feedback-001.md"

# Step 4: Generate (iteration 2 — reads feedback)
codex -p --model deep "You are a Generator. Read spec.md and feedback-001.md. Address all issues. Improve the scores."

# Repeat steps 3-4 until pass threshold met

Evolution Across Model Capabilities

The harness should simplify as models improve. Following OpenAI's evolution:

Stage 1 — Weaker Models (Standard-class)

• Full sprint decomposition required
• Context resets between sprints (avoid context anxiety)
• 2-agent minimum: Initializer + Coding Agent
• Heavy scaffolding compensates for model limitations

Stage 2 — Capable Models (Deep 4.5-class)

• Full 3-agent harness: Planner + Generator + Evaluator
• Sprint contracts before each implementation phase
• 10-sprint decomposition for complex apps
• Context resets still useful but less critical

Stage 3 — Frontier Models (Deep 4.6-class)

• Simplified harness: single planning pass, continuous generation
• Evaluation reduced to single end-pass (model is smarter)
• No sprint structure needed
• Automatic compaction handles context growth

Key principle: Every harness component encodes an assumption about what the model can't do alone. When models improve, re-test those assumptions. Strip away what's no longer needed.

Configuration

Environment Variables

Evaluation Modes

Anti-Patterns

1. Evaluator too lenient — If the evaluator passes everything on iteration 1, your rubric is too generous. Tighten scoring criteria and add explicit penalties for common AI patterns.

1. Generator ignoring feedback — Ensure feedback is passed as a file, not inline. The generator should read feedback-NNN.md at the start of each iteration.

1. Infinite loops — Always set GAN_MAX_ITERATIONS. If the generator can't improve past a score plateau after 3 iterations, stop and flag for human review.

1. Evaluator testing superficially — The evaluator must use Playwright to interact with the live app, not just screenshot it. Click buttons, fill forms, test error states.

1. Evaluator praising its own fixes — Never let the evaluator suggest fixes and then evaluate those fixes. The evaluator only critiques; the generator fixes.

1. Context exhaustion — For long sessions, use Codex Agent SDK's automatic compaction or reset context between major phases.

Results: What to Expect

Based on OpenAI's published results:

The tradeoff is clear: ~20x more time and cost for a qualitative leap in output quality. This is for projects where quality matters.

References

• OpenAI: Harness Design for Long-Running Apps — Original paper by Prithvi Rajasekaran
• Epsilla: The GAN-Style Agent Loop — Architecture deconstruction
• Martin Fowler: Harness Engineering — Broader industry context
• OpenAI: Harness Engineering — OpenAI's parallel work