GAN-inspired Generator-Evaluator agent harness for building high-quality applications autonomously. Based on Anthropic's March 2026 harness design paper. Use when a feature should be built autonomously through generator and evaluator iteration until it clears a quality bar.

GAN Style Harness Skill Inspired by [Anthropic's Harness Design for Long Running Application Development](https://www.anthropic.com/engineering/harness design long running apps) (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 claude 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 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: Sonnet by default; raise via GAN PLANNER MODEL=opus for deeper 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: Sonnet by default; raise via GAN GENERATOR MODEL=opus for maximum 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: Sonnet by default; raise via GAN EVALUATOR MODEL=opus for stronger judgment + tool use Evaluation Criteria The default four criteria, each scored 1 10: Scoring Weighted score = sum of (criterion score weight) Pass threshold = 7.0 (configurable) Max iterations = 15 (configurable, typically 5 15 sufficient) Usage Via Command Via Shell Script Via Claude Code (Manual) Evolution Across Model Capabilities The harness should simplify as models improve. Following Anthropic's evolution: Stage 1 — Weaker Models (Sonnet 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 (Opus 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 (Opus 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 Variable Default Description GAN MAX ITERATIONS 15 Maximum generator evaluator cycles GAN PASS THRESHOLD 7.0 Weighted score to pass (1 10) GAN PLANNER MODEL sonnet Model for planning agent GAN GENERATOR MODEL sonnet Model for generator agent GAN EVALUATOR MODEL sonnet Model for evaluator agent GAN EVAL CRITERIA design,originality,craft,functionality Comma separated criteria GAN DEV SERVER PORT 3000 Port for the live app GAN DEV SERVER CMD npm run dev Command to start dev server GAN PROJECT DIR . Project working directory GAN SKIP PLANNER false Skip planner, use spec directly GAN EVAL MODE playwright playwright , screenshot , or code only Evaluation Modes Mode Tools Best For playwright Browser MCP + live interaction Full stack apps with UI screenshot Screenshot + visual analysis Static sites, design only code only Tests + linting + build APIs, libraries, CLI tools 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. 2. 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. 3. 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. 4. 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. 5. Evaluator praising its own fixes — Never let the evaluator suggest fixes and then evaluate those fixes. The evaluator only critiques; the generator fixes. 6. Context exhaustion — For long sessions, use Claude Agent SDK's automatic compaction or reset context between major phases. Results: What to Expect Based on Anthropic's published results: Metric Solo Agent GAN Harness Improvement Time 20 min 4 6 hours 12 18x longer Cost $9 $125 200 14 22x more Quality Barely functional Production ready Phase change Core features Broken All working N/A Design Generic AI slop Distinctive, polished N/A The tradeoff is clear: ~20x more time and cost for a qualitative leap in output quality. This is for projects where quality matters. References [Anthropic: Harness Design for Long Running Apps](https://www.anthropic.com/engineering/harness design long running apps) — Original paper by Prithvi Rajasekaran [Epsilla: The GAN Style Agent Loop](https://www.epsilla.com/blogs/anthropic harness engineering multi agent gan architecture) — Architecture deconstruction [Martin Fowler: Harness Engineering](https://martinfowler.com/articles/exploring gen ai/harness engineering.html) — Broader industry context [OpenAI: Harness Engineering](https://openai.com/index/harness engineering/) — OpenAI's parallel work

gan-style-harness