SuperClaude/docs/research/reflexion-integration-2025.md

Reflexion Framework Integration - PM Agent

Date: 2025-10-17 Purpose: Integrate Reflexion self-reflection mechanism into PM Agent Source: Reflexion: Language Agents with Verbal Reinforcement Learning (2023, arXiv)

---

概要

Reflexionは、LLMエージェントが自分の行動を振り返り、エラーを検出し、次の試行で改善するフレームワーク。

核心メカニズム

Traditional Agent:
  Action → Observe → Repeat
  問題: 同じ間違いを繰り返す

Reflexion Agent:
  Action → Observe → Reflect → Learn → Improved Action
  利点: 自己修正、継続的改善

---

PM Agent統合アーキテクチャ

1. Self-Evaluation (自己評価)

タイミング: 実装完了後、完了報告前

Purpose: 自分の実装を客観的に評価

Questions:
  ❓ "この実装、本当に正しい？"
  ❓ "テストは全て通ってる？"
  ❓ "思い込みで判断してない？"
  ❓ "ユーザーの要件を満たしてる？"

Process:
  1. 実装内容を振り返る
  2. テスト結果を確認
  3. 要件との照合
  4. 証拠の有無確認

Output:
  - 完了判定 (✅ / ❌)
  - 不足項目リスト
  - 次のアクション提案

2. Self-Reflection (自己反省)

タイミング: エラー発生時、実装失敗時

Purpose: なぜ失敗したのかを理解する

Reflexion Example (Original Paper):
  "Reflection: I searched the wrong title for the show,
   which resulted in no results. I should have searched
   the show's main character to find the correct information."

PM Agent Application:
  "Reflection:
   ❌ What went wrong: JWT validation failed
   🔍 Root cause: Missing environment variable SUPABASE_JWT_SECRET
   💡 Why it happened: Didn't check .env.example before implementation
   ✅ Prevention: Always verify environment setup before starting
   📝 Learning: Add env validation to startup checklist"

Storage:
  → docs/memory/solutions_learned.jsonl
  → docs/mistakes/[feature]-YYYY-MM-DD.md
  → mindbase (if available)

3. Memory Integration (記憶統合)

Purpose: 過去の失敗から学習し、同じ間違いを繰り返さない

Error Occurred:
  1. Check Past Errors (Smart Lookup):
     IF mindbase available:
       → mindbase.search_conversations(
           query=error_message,
           category="error",
           limit=5
         )
       → Semantic search for similar past errors

     ELSE (mindbase unavailable):
       → Grep docs/memory/solutions_learned.jsonl
       → Grep docs/mistakes/ -r "error_message"
       → Text-based pattern matching

  2. IF similar error found:
     ✅ "⚠️ 過去に同じエラー発生済み"
     ✅ "解決策: [past_solution]"
     ✅ Apply known solution immediately
     → Skip lengthy investigation

  3. ELSE (new error):
     → Proceed with root cause investigation
     → Document solution for future reference

---

実装パターン

Pattern 1: Pre-Implementation Reflection

Before Starting:
  PM Agent Internal Dialogue:
    "Am I clear on what needs to be done?"
    → IF No: Ask user for clarification
    → IF Yes: Proceed

    "Do I have sufficient information?"
    → Check: Requirements, constraints, architecture
    → IF No: Research official docs, patterns
    → IF Yes: Proceed

    "What could go wrong?"
    → Identify risks
    → Plan mitigation strategies

Pattern 2: Mid-Implementation Check

During Implementation:
  Checkpoint Questions (every 30 min OR major milestone):
    ❓ "Am I still on track?"
    ❓ "Is this approach working?"
    ❓ "Any warnings or errors I'm ignoring?"

  IF deviation detected:
    → STOP
    → Reflect: "Why am I deviating?"
    → Reassess: "Should I course-correct or continue?"
    → Decide: Continue OR restart with new approach

Pattern 3: Post-Implementation Reflection

After Implementation:
  Completion Checklist:
    ✅ Tests all pass (actual results shown)
    ✅ Requirements all met (checklist verified)
    ✅ No warnings ignored (all investigated)
    ✅ Evidence documented (test outputs, code changes)

  IF checklist incomplete:
    → ❌ NOT complete
    → Report actual status honestly
    → Continue work

  IF checklist complete:
    → ✅ Feature complete
    → Document learnings
    → Update knowledge base

---

Hallucination Prevention Strategies

Strategy 1: Evidence Requirement

Principle: Never claim success without evidence

Claiming "Complete":
  MUST provide:
    1. Test Results (actual output)
    2. Code Changes (file list, diff summary)
    3. Validation Status (lint, typecheck, build)

  IF evidence missing:
    → BLOCK completion claim
    → Force verification first

Strategy 2: Self-Check Questions

Principle: Question own assumptions systematically

Before Reporting:
  Ask Self:
    ❓ "Did I actually RUN the tests?"
    ❓ "Are the test results REAL or assumed?"
    ❓ "Am I hiding any failures?"
    ❓ "Would I trust this implementation in production?"

  IF any answer is negative:
    → STOP reporting success
    → Fix issues first

Strategy 3: Confidence Thresholds

Principle: Admit uncertainty when confidence is low

Confidence Assessment:
  High (90-100%):
    → Proceed confidently
    → Official docs + existing patterns support approach

  Medium (70-89%):
    → Present options
    → Explain trade-offs
    → Recommend best choice

  Low (<70%):
    → STOP
    → Ask user for guidance
    → Never pretend to know

---

Token Budget Integration

Challenge: Reflection costs tokens

Solution: Budget-aware reflection based on task complexity

Simple Task (typo fix):
  Reflection Budget: 200 tokens
  Questions: "File edited? Tests pass?"

Medium Task (bug fix):
  Reflection Budget: 1,000 tokens
  Questions: "Root cause identified? Tests added? Regression prevented?"

Complex Task (feature):
  Reflection Budget: 2,500 tokens
  Questions: "All requirements met? Tests comprehensive? Integration verified? Documentation updated?"

Anti-Pattern:
  ❌ Unlimited reflection → Token explosion
  ✅ Budgeted reflection → Controlled cost

---

Success Metrics

Quantitative

Hallucination Detection Rate:
  Target: >90% (Reflexion paper: 94%)
  Measure: % of false claims caught by self-check

Error Recurrence Rate:
  Target: <10% (same error repeated)
  Measure: % of errors that occur twice

Confidence Accuracy:
  Target: >85% (confidence matches reality)
  Measure: High confidence → success rate

Qualitative

Culture Change:
  ✅ "わからないことをわからないと言う"
  ✅ "嘘をつかない、証拠を示す"
  ✅ "失敗を認める、次に改善する"

Behavioral Indicators:
  ✅ User questions reduce (clear communication)
  ✅ Rework reduces (first attempt accuracy increases)
  ✅ Trust increases (honest reporting)

---

Implementation Checklist

• Self-Check質問システム (完了前検証)
• Evidence Requirement (証拠要求)
• Confidence Scoring (確信度評価)
• Reflexion Pattern統合 (自己反省ループ)
• Token-Budget-Aware Reflection (予算制約型振り返り)
• 実装例とアンチパターン文書化
• workflow_metrics.jsonl統合
• テストと検証

---

References

1. Reflexion: Language Agents with Verbal Reinforcement Learning

   • Authors: Noah Shinn et al.
   • Year: 2023
   • Key Insight: Self-reflection enables 94% error detection rate

2. Self-Evaluation in AI Agents

   • Source: Galileo AI (2024)
   • Key Insight: Confidence scoring reduces hallucinations

3. Token-Budget-Aware LLM Reasoning

   • Source: arXiv 2412.18547 (2024)
   • Key Insight: Budget constraints enable efficient reflection

---

End of Report