quantum-ai-v2/five_whys_analyzer/n8n_workflows/README.md

Five Whys Analyzer Agent - N8N Workflow

Overview

This directory contains the N8N workflow configuration for the Five Whys Analyzer Agent, which conducts systematic root cause analysis using the proven Five Whys methodology.

Workflow Files

• workflow.json - Production workflow for N8N import
• workflow_dev.json - Development/testing version (optional)
• workflow_backup.json - Backup version for disaster recovery

Webhook Configuration

• Webhook URL: Configured via N8N_WEBHOOK_FIVE_WHYS environment variable
• HTTP Method: POST
• Expected Data Format:

  {
    "problem": "Website conversion rate dropped by 30%",
    "context": "E-commerce site, occurred after recent update",
    "industry": "retail",
    "stakeholders": ["marketing team", "dev team", "customers"],
    "additional_info": "Peak season, mobile traffic increased"
  }
  

Setup Instructions

1. Import Workflow to N8N

1. Open your N8N instance
2. Click "Import from File" or "Import from URL"
3. Upload the workflow.json file
4. Configure credentials (OpenAI API key, etc.)
5. Activate the workflow

2. Configure Webhook URL

1. Copy the webhook URL from N8N
2. Set environment variable: N8N_WEBHOOK_FIVE_WHYS=https://your-n8n.com/webhook/five-whys
3. Restart your Django application

3. Test the Workflow

# Test via Django application
python manage.py test_webhook five_whys_analyzer

# Or test directly via curl
curl -X POST https://your-n8n.com/webhook/five-whys \
  -H "Content-Type: application/json" \
  -d '{"problem":"Customer complaints increased","context":"After product launch","industry":"saas"}'

Workflow Components

• Webhook Node: Receives requests from Django application
• Problem Analysis: Systematic Five Whys questioning process
• AI Processing: Uses OpenAI GPT-4 for intelligent analysis
• Root Cause Identification: Identifies underlying causes
• Action Planning: Generates actionable recommendations
• Response Node: Returns structured analysis results
• Error Handling: Manages analysis failures and edge cases

Expected Response Format

{
  "success": true,
  "analysis": {
    "problem_statement": "Website conversion rate dropped by 30%",
    "five_whys_sequence": [
      {
        "question": "Why did the conversion rate drop?",
        "answer": "Users are abandoning checkout process"
      },
      {
        "question": "Why are users abandoning checkout?",
        "answer": "Page loading times increased significantly"
      },
      {
        "question": "Why did loading times increase?",
        "answer": "New payment integration is slow"
      },
      {
        "question": "Why is the payment integration slow?",
        "answer": "Third-party API has latency issues"
      },
      {
        "question": "Why wasn't this tested before deployment?",
        "answer": "Load testing didn't include payment flow"
      }
    ],
    "root_causes": [
      "Inadequate load testing procedures",
      "Third-party API performance issues",
      "Missing performance monitoring for payment flow"
    ],
    "immediate_actions": [
      "Switch to backup payment provider",
      "Optimize payment integration code",
      "Add performance monitoring"
    ],
    "long_term_solutions": [
      "Implement comprehensive load testing",
      "Establish SLA requirements for third parties",
      "Create performance regression testing"
    ],
    "prevention_strategies": [
      "Include all critical paths in testing",
      "Monitor third-party dependencies",
      "Establish performance baselines"
    ]
  },
  "confidence_level": "high",
  "recommended_timeline": "immediate: 1-2 days, long-term: 2-4 weeks"
}

Analysis Categories

• Technical Issues: Software bugs, performance problems
• Process Problems: Workflow inefficiencies, communication gaps
• Human Factors: Training gaps, resource constraints
• External Factors: Market changes, supplier issues
• System Issues: Infrastructure, tools, technology stack

Industry Applications

• Software Development (bugs, performance)
• Manufacturing (quality issues, downtime)
• Customer Service (complaint resolution)
• Marketing (campaign performance)
• Operations (process inefficiencies)
• Sales (conversion problems)

Troubleshooting

• Shallow analysis: Provide more context and stakeholder info
• Generic recommendations: Include industry-specific details
• Missing root causes: Ensure problem description is comprehensive
• Incomplete action items: Specify timeline and resource constraints

Best Practices

• Provide comprehensive problem context
• Include all relevant stakeholders
• Specify industry for targeted analysis
• Be specific about problem symptoms
• Include timeline and impact information
• Follow up on recommended actions
• Document lessons learned for future reference