Design Iteration Loop

MoAI-ADK 프로젝트에서는 .moai/config/sections/design.yaml과 기존 스프린트 산출물을 읽는다. Claude Cowork·ChatGPT Work 데스크톱에서는 사용자가 제공한 브리프와 실제 시안으로 같은 평가 항목을 점검할 수 있다. 설정 파일이나 검사 도구가 없으면 그 항목을 미검증으로 기록하고 점수나 PASS를 만들어 내지 않는다.

design-iteration-loop

Implements the Builder-Evaluator GAN loop for iterative design quality improvement. Absorbed from the retired v2.x design constitution Section 11 and Section 12 (per the design constitution absorption policy). Integrates Sprint Contract Protocol, 4-dimension scoring, stagnation detection, and Evaluator Leniency Prevention.

In a MoAI-ADK project, read loop parameters from .moai/config/sections/design.yaml; the values below describe the current project configuration and are not portable defaults. In a desktop conversation without that file, agree on criteria with the user and report observations without inventing thresholds.

Quick Reference

Loop Parameters (from design.yaml)

design.gan_loop:
  max_iterations: 5          # Maximum Builder-Evaluator cycles
  pass_threshold: 0.75       # Score >= this value to exit loop
  escalation_after: 3        # Escalate to user after N iterations without passing
  improvement_threshold: 0.05  # Minimum score delta per iteration
  strict_mode: false         # If true, each must-pass criterion must pass individually
  sprint_contract:
    enabled: true
    required_harness_levels: [thorough]
    optional_harness_levels: [standard]
    artifact_dir: ".moai/sprints"
    max_negotiation_rounds: 2

4-Dimension Evaluation

The current design.yaml does not define weights for these dimensions. Do not present 30/25/25/20 or any other split as configured. The ADK evaluator owns the overall score; cite its actual rubric and result. In a standalone desktop review, report each observed dimension and gaps without a synthetic overall score.

In the ADK loop, pass requires the evaluator's measured overall score to reach pass_threshold and every contracted must-pass criterion to pass. Strict mode adds the constitution's two-iteration minimum. A standalone desktop review without a configured evaluator does not issue this numeric PASS.

Implementation Guide

GAN Loop Execution Flow

Phase 1: Sprint Contract (when required by harness level)

Required when harness_level == thorough. Optional when harness_level == standard and user opts in. Skipped when harness_level == minimal.

Sprint Contract generation:

1. Evaluator analyzes the BRIEF document and current iteration scope.
2. Evaluator produces the Sprint Contract document:
   • acceptance_checklist: concrete, testable criteria for this iteration
   • priority_dimension: which of the 4 dimensions to focus on
   • test_scenarios: specific verification steps
   • pass_conditions: minimum score per criterion
3. Builder reviews the contract:
   • Accept: proceed with implementation
   • Request adjustment: propose alternatives (max max_negotiation_rounds rounds)
4. Contract is saved to design.gan_loop.sprint_contract.artifact_dir/sprint-N.json

Constraint: Evaluator must not score on criteria outside the Sprint Contract. Builder must not claim criteria as met without evidence.

Phase 2: Builder Execution

Builder implements based on:

• Accepted Sprint Contract (if present)
• BRIEF document
• Copy JSON from design-copywriting
• Design tokens from design-brand-system or design-workflow (Path A handler)

Builder outputs: code files, rendered previews (if Playwright available), implementation notes.

Phase 3: Evaluator Scoring

Evaluator scores against the 4 dimensions using the Evaluator Leniency Prevention mechanisms:

1. Rubric Anchoring: Score each dimension against the rubric (0.25 increments) with explicit justification. Scores without rubric reference are invalid.
2. Evidence-Only Verdicts: No PASS without concrete evidence (screenshot, test output, code reference).
3. Anti-Pattern Cross-check: Check known anti-patterns before finalizing. Any detected anti-pattern caps the relevant dimension score at 0.50.
4. Must-Pass Firewall: Copy integrity, mobile viewport, and WCAG AA are must-pass criteria. Failure in any must-pass = overall FAIL regardless of other scores.

Output: evaluation-report-N.json in sprint_contract.artifact_dir.

Phase 4: Loop Decision

if overall_score >= pass_threshold:
    EXIT LOOP → proceed to next phase
elif iteration >= max_iterations:
    ESCALATE → present failure report to user
elif stagnation_detected:
    ESCALATE → present stagnation options
else:
    ITERATE → pass feedback to Builder, increment N

Phase 5: Iteration Feedback

If looping back:

1. Evaluator generates targeted feedback per failed criterion.
2. Builder receives the feedback and previous Sprint Contract.
3. Previously passed criteria carry forward (no regression allowed).
4. New Sprint Contract is generated for failed criteria only.

Stagnation Detection

Stagnation is detected when the score improvement between consecutive iterations is below improvement_threshold for 2 or more iterations.

Tracking:

• After each iteration, record {iteration: N, score: X} in the sprint artifact.
• Calculate delta = score[N] - score[N-1].
• If delta < improvement_threshold for the last 2 iterations, flag stagnation.

When stagnation is detected, present the findings through the available user question channel with three options:

1. Continue with current approach (Evaluator tries a different dimension focus)
2. Adjust criteria (user provides guidance or relaxes constraints)
3. Abort loop (accept current output as-is)

The escalation trigger at escalation_after iterations applies independently: if 3 iterations pass without a PASS score, escalate regardless of stagnation state.