Debugging Agent

Self-Improving Agent System의 핵심 컴포넌트

다른 모든 agent의 로그를 분석하여 문제를 발견하고 개선안을 제안합니다.

📋 Core Workflow

1. Log Collection (로그 수집)

python backend/ai/skills/system/debugging-agent/scripts/log_reader.py
--days 1
--categories system,war-room,analysis

수집 대상:

• backend/ai/skills/logs///execution-*.jsonl

• backend/ai/skills/logs///errors-*.jsonl

• backend/ai/skills/logs///performance-*.jsonl

Output:

{ "agents": ["signal-consolidation", "war-room-debate", ...], "total_executions": 50, "total_errors": 3, "time_range": "2025-12-25 to 2025-12-26" }

2. Pattern Detection (패턴 감지)

python backend/ai/skills/system/debugging-agent/scripts/pattern_detector.py
--input logs_summary.json
--output patterns.json

감지 패턴:

A. Recurring Errors (반복 에러)

• 조건: 동일한 error type이 24시간 내 3회 이상

• 예시: TypeError: missing required positional argument (3회)

• 우선순위: HIGH

B. Performance Degradation (성능 저하)

• 조건: duration_ms가 baseline 대비 2배 이상

• 예시: 평균 1000ms → 최근 2500ms

• 우선순위: MEDIUM

C. High Error Rate (높은 에러율)

• 조건: error rate > 5%

• 예시: 50 executions, 4 errors = 8%

• 우선순위: CRITICAL

D. API Rate Limits (API 제한)

• 조건: "rate limit" 관련 에러 5회 이상

• 우선순위: HIGH

Output:

{ "patterns": [ { "type": "recurring_error", "agent": "war-room-debate", "error_type": "TypeError", "count": 3, "impact": "CRITICAL", "first_seen": "2025-12-25T18:30:00", "last_seen": "2025-12-26T09:15:00" } ] }

3. Context Synthesis (맥락 통합)

관련 agent의 SKILL.md 를 읽어서 컨텍스트 파악:

Read related skills

cat backend/ai/skills/war-room/war-room-debate/SKILL.md cat backend/api/war_room_router.py

파악 내용:

• Agent의 역할과 책임

• 입력/출력 형식

• 의존성 (DB, APIs, etc.)

• 최근 변경사항

4. Improvement Proposal (개선안 생성)

python backend/ai/skills/system/debugging-agent/scripts/improvement_proposer.py
--patterns patterns.json
--output proposals/proposal-20251226-100822.md

Proposal 포맷:

Improvement Proposal: Fix War Room TypeError

Generated: 2025-12-26 10:08:22
Agent: war-room-debate
Priority: CRITICAL
Confidence: 87%

🔍 Issue Summary

Pattern Detected: Recurring Error (3 occurrences in 24h)

Error:

TypeError: missing required positional argument for AIDebateSession

Impact:

• War Room debates failing
• No trading signals generated
• User experience degraded

📊 Root Cause Analysis

Evidence:

1. Error occurs in war_room_router.py:L622
2. AIDebateSession.__init__() called with missing argument
3. Recent code change added new required field

Root Cause: Schema mismatch between AIDebateSession model and router code.

💡 Proposed Solution

Option 1: Add Missing Argument (Recommended)

File: backend/api/war_room_router.py

# Line 622 - Add missing argument
session = AIDebateSession(
    ticker=ticker,
    consensus_action=pm_decision["consensus_action"],
    # ... existing fields ...
    dividend_risk_vote=next((v["action"] for v in votes if v["agent"] == "dividend_risk"), None),  # ← ADD THIS
    created_at=datetime.now()
)

Confidence: 90% (high evidence)

Option 2: Make Field Optional

Alternatively, update the model to make the field optional.

Confidence: 70% (lower impact but safer)

🎯 Expected Impact

• ✅ Eliminates TypeError
• ✅ War Room debates resume
• ✅ Trading signals restored
• ⚠️ Requires testing with all agents

🧪 Verification Plan

1. Apply fix to war_room_router.py
2. Run War Room debate: POST /api/war-room/debate {"ticker": "AAPL"}
3. Verify no TypeError
4. Check logs for successful execution

📝 Risk Assessment

Risk Level: LOW

Potential Issues:

• May need to update other agent votes similarly
• Database migration if schema changed

Rollback Plan:

• Revert commit if issues arise
• Monitor error logs for 24h

Confidence Breakdown:

• Error Reproducibility: 100% (3/3 occurrences)
• Historical Success: 80% (similar fixes worked)
• Impact Clarity: 90% (clear user impact)
• Root Cause Evidence: 85% (stack trace clear)
• Solution Simplicity: 85% (1-line fix)

Overall Confidence: 87%

🎯 Confidence Scoring (5 Metrics)

Proposal confidence는 5가지 메트릭의 가중 평균:

Error Reproducibility (30%)

• 100% if error occurs every time

• 0% if random/sporadic

Historical Success (25%)

• Similar fixes worked before?

• Based on past proposals

Impact Clarity (20%)

• Clear user/system impact?

• Measurable consequences?

Root Cause Evidence (15%)

• Stack trace available?

• Clear error message?

Solution Simplicity (10%)

• Simple 1-line fix vs complex refactor

• Lower risk = higher confidence

Formula:

confidence = ( reproducibility * 0.30 + historical_success * 0.25 + impact_clarity * 0.20 + root_cause_evidence * 0.15 + solution_simplicity * 0.10 )

🔄 Usage Examples

Manual Trigger

Analyze recent logs

python backend/ai/skills/system/debugging-agent/scripts/log_reader.py --days 1

Detect patterns

python backend/ai/skills/system/debugging-agent/scripts/pattern_detector.py

Generate proposals

python backend/ai/skills/system/debugging-agent/scripts/improvement_proposer.py

Scheduled Execution (via orchestrator)

scripts/run_debugging_agent.py

import schedule

def run_debugging_agent(): subprocess.run(["python", "backend/ai/skills/system/debugging-agent/scripts/log_reader.py"]) subprocess.run(["python", "backend/ai/skills/system/debugging-agent/scripts/pattern_detector.py"]) subprocess.run(["python", "backend/ai/skills/system/debugging-agent/scripts/improvement_proposer.py"])

schedule.every(30).minutes.do(run_debugging_agent)

📁 Output Structure

backend/ai/skills/logs/system/debugging-agent/ ├── execution-2025-12-26.jsonl # Debugging agent's own logs ├── errors-2025-12-26.jsonl └── proposals/ ├── proposal-20251226-100822.md # Improvement proposal ├── proposal-20251226-103045.md └── accepted/ └── proposal-20251226-100822.md # User accepted

⚠️ Important Notes

• Read-Only Access: Debugging Agent는 로그만 읽고 코드는 수정하지 않음

• User Approval Required: 모든 제안은 사용자 승인 필요

• Audit Trail: 모든 제안과 결과는 proposals/ 디렉토리에 보관

• Safety First: Confidence < 70%인 제안은 경고 표시

🚀 Next Steps

After Phase 2 complete:

• Phase 3: Skill Orchestrator (scheduling, notifications)

• (Optional) Phase 4: CI/CD Integration (auto-apply patches)

Created: 2025-12-26

Version: 1.0

Status: In Development