Error Monitoring & AI-Powered Auto-Fix Strategy

Comprehensive error monitoring strategy with AI integration for automated ticket creation and PR generation.

Executive Summary

Recommended Solution: Sentry with AI integration for:

• ✅ Error tracking and monitoring
• ✅ Automated GitHub issue creation
• ✅ AI-powered code fixes
• ✅ Automated PR generation

Tool Comparison

Sentry ⭐ RECOMMENDED

Pros:

• ✅ Excellent API for programmatic access
• ✅ Webhook support for real-time error notifications
• ✅ Built-in GitHub integration
• ✅ Rich error context (stack traces, breadcrumbs, user context)
• ✅ Works across backend, web, and mobile
• ✅ Performance monitoring included
• ✅ Session replay (paid feature)
• ✅ Great AI integration capabilities
• ✅ Free tier: 5,000 events/month

Cons:

• ⚠️ Can get expensive at scale
• ⚠️ Session replay requires paid plan

Best For:

• Error tracking with AI integration
• Automated workflows
• Multi-platform monitoring
• Performance monitoring

Pricing: Free tier → $26/month (Team) → Custom (Business)

LogRocket

Pros:

• ✅ Excellent session replay
• ✅ Great for frontend debugging
• ✅ User session recordings
• ✅ Network request monitoring

Cons:

• ❌ Limited API for automation
• ❌ Less suitable for backend error tracking
• ❌ No built-in GitHub integration
• ❌ More expensive
• ❌ Harder to integrate with AI workflows

Best For:

• Frontend-focused debugging
• User session analysis
• Visual debugging

Pricing: $99/month (Starter) → $249/month (Professional)

Application Insights (Azure)

Pros:

• ✅ Native Azure integration
• ✅ Good .NET support
• ✅ AI-powered insights

Cons:

• ❌ You're using AWS, not Azure
• ❌ Less flexible for custom workflows
• ❌ Limited GitHub integration

Best For:

• Azure-hosted applications
• Microsoft ecosystem

Pricing: Pay-as-you-go

Datadog

Pros:

• ✅ Full observability platform
• ✅ Excellent API
• ✅ Great for infrastructure monitoring

Cons:

• ❌ Expensive
• ❌ Overkill for error monitoring alone
• ❌ Complex setup

Best For:

• Full-stack observability
• Large-scale applications

Pricing: $31/month (Pro) → Custom (Enterprise)

Rollbar

Pros:

• ✅ Good API
• ✅ GitHub integration
• ✅ Simple setup

Cons:

• ❌ Less feature-rich than Sentry
• ❌ Smaller ecosystem

Best For:

• Simple error tracking
• Small teams

Pricing: Free → $19/month → Custom

Recommendation: Sentry

Why Sentry is the best choice for your use case:

1. Excellent API - Perfect for AI integration
2. Webhook Support - Real-time error notifications
3. GitHub Integration - Built-in issue creation
4. Rich Context - Stack traces, breadcrumbs, user info
5. Multi-Platform - Backend (.NET), Web (React), Mobile (React Native)
6. AI-Friendly - Easy to extract error data for AI processing

AI Integration Architecture

Workflow Overview

Error Occurs
    ↓
Sentry Captures Error
    ↓
Webhook Triggers → GitHub Actions / AWS Lambda
    ↓
AI Analyzes Error (OpenAI/Anthropic)
    ↓
Create GitHub Issue (with AI analysis)
    ↓
AI Generates Fix (with code changes)
    ↓
Create Pull Request (with fix)
    ↓
Review & Merge

Components

1. Sentry - Error capture and monitoring
2. Webhook Handler - Receives Sentry webhooks (GitHub Actions or AWS Lambda)
3. AI Service - Analyzes errors and generates fixes (OpenAI/Anthropic)
4. GitHub Integration - Creates issues and PRs
5. Code Generator - Applies AI-generated fixes

Implementation Plan

Phase 1: Sentry Setup (Week 1)

Backend (.NET)

1. Install Sentry SDK:

dotnet add package Sentry.AspNetCore
dotnet add package Sentry.EntityFramework

2. Configure Sentry in Program.cs:

using Sentry;

builder.WebHost.UseSentry(options =>
{
    options.Dsn = builder.Configuration["Sentry:Dsn"];
    options.Environment = builder.Environment.EnvironmentName;
    options.TracesSampleRate = builder.Environment.IsProduction() ? 0.1 : 1.0;
    options.SendDefaultPii = false; // Don't send PII
    
    // Add user context
    options.BeforeSend = (sentryEvent) =>
    {
        // Add custom context
        sentryEvent.SetTag("service", "api");
        return sentryEvent;
    };
});

// Add Sentry to logging
builder.Logging.AddSentry();

3. Add to appsettings.json:

{
  "Sentry": {
    "Dsn": "https://your-dsn@sentry.io/project-id",
    "Environment": "Development",
    "TracesSampleRate": 1.0
  }
}

4. Add Sentry middleware:

app.UseSentryTracing();

Web App (React/Next.js)

1. Install Sentry:

npm install @sentry/nextjs

2. Initialize Sentry:

// sentry.client.config.ts
import * as Sentry from "@sentry/nextjs";

Sentry.init({
  dsn: process.env.NEXT_PUBLIC_SENTRY_DSN,
  environment: process.env.NODE_ENV,
  tracesSampleRate: 1.0,
  beforeSend(event, hint) {
    // Add custom context
    event.tags = {{ ...event.tags, service: "web" }};
    return event;
  },
});

Mobile App (React Native)

1. Install Sentry:

npm install @sentry/react-native

2. Initialize Sentry:

import * as Sentry from "@sentry/react-native";

Sentry.init({
  dsn: process.env.SENTRY_DSN,
  environment: __DEV__ ? "development" : "production",
  tracesSampleRate: 1.0,
});

Phase 2: Webhook Setup (Week 1-2)

Option A: GitHub Actions (Recommended)

Create .github/workflows/sentry-webhook.yml:

name: Sentry Error Handler

on:
  repository_dispatch:
    types: [sentry-error]

jobs:
  handle-error:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Setup .NET
        uses: actions/setup-dotnet@v3
        with:
          dotnet-version: '9.0.x'
      
      - name: Analyze Error with AI
        id: analyze
        run: |
          # Call AI service to analyze error
          # Returns: error_summary, suggested_fix, code_changes
      
      - name: Create GitHub Issue
        uses: actions/github-script@v6
        with:
          script: |
            github.rest.issues.create({
              owner: context.repo.owner,
              repo: context.repo.repo,
              title: `[Auto] ${{steps.analyze.outputs.error_summary}}`,
              body: `## Error Details
              ${{steps.analyze.outputs.error_details}}
              
              ## Suggested Fix
              ${{steps.analyze.outputs.suggested_fix}}
              
              ## Code Changes
              \`\`\`
              ${{steps.analyze.outputs.code_changes}}
              \`\`\`
              `,
              labels: ['bug', 'auto-generated', 'ai-suggested']
            });
      
      - name: Create Pull Request
        if: steps.analyze.outputs.has_fix == 'true'
        run: |
          # Create PR with AI-generated fix

Option B: AWS Lambda (Alternative)

Create Lambda function to handle Sentry webhooks:

// SentryWebhookHandler.cs
public class SentryWebhookHandler
{
    public async Task<APIGatewayProxyResponse> Handle(SentryWebhookEvent evt)
    {
        // Analyze error with AI
        var analysis = await AnalyzeErrorWithAI(evt);
        
        // Create GitHub issue
        await CreateGitHubIssue(analysis);
        
        // Create PR if fix available
        if (analysis.HasFix)
        {
            await CreatePullRequest(analysis);
        }
        
        return new APIGatewayProxyResponse { StatusCode = 200 };
    }
}

Phase 3: AI Integration (Week 2-3)

AI Error Analysis Service

// Services/AIErrorAnalysisService.cs
public class AIErrorAnalysisService
{
    private readonly IOpenAIService _openAI;
    
    public async Task<ErrorAnalysis> AnalyzeErrorAsync(SentryEvent sentryEvent)
    {
        var prompt = BuildAnalysisPrompt(sentryEvent);
        var response = await _openAI.ChatCompletionAsync(new ChatCompletionRequest
        {
            Model = "gpt-4",
            Messages = new[]
            {
                new ChatMessage
                {
                    Role = "system",
                    Content = @"You are an expert .NET developer. Analyze errors and suggest fixes.
                    Return JSON with: error_summary, root_cause, suggested_fix, code_changes, confidence"
                },
                new ChatMessage
                {
                    Role = "user",
                    Content = prompt
                }
            },
            ResponseFormat = ChatCompletionResponseFormat.Json
        });
        
        return JsonSerializer.Deserialize<ErrorAnalysis>(response.Choices[0].Message.Content);
    }
    
    private string BuildAnalysisPrompt(SentryEvent evt)
    {
        return $@"
        Analyze this error and suggest a fix:
        
        Error: {{evt.Message}}
        Stack Trace: {{evt.Exception?.StackTrace}}
        File: {{evt.Exception?.FileName}}
        Line: {{evt.Exception?.LineNumber}}
        Context: {{JsonSerializer.Serialize(evt.Contexts)}}
        User: {{evt.User?.Username}}
        Environment: {{evt.Environment}}
        
        Codebase: ASP.NET Core 9.0, PostgreSQL, AWS Cognito
        
        Provide:
        1. Root cause analysis
        2. Suggested fix with code changes
        3. Confidence level (0-100)
        ";
    }
}

AI Code Fix Generator

// Services/AICodeFixService.cs
public class AICodeFixService
{
    public async Task<CodeFix> GenerateFixAsync(ErrorAnalysis analysis, string filePath)
    {
        var fileContent = await File.ReadAllTextAsync(filePath);
        
        var prompt = $@"
        Fix this error in the following code:
        
        Error: {{analysis.ErrorSummary}}
        Root Cause: {{analysis.RootCause}}
        Suggested Fix: {{analysis.SuggestedFix}}
        
        Current Code:
        ```csharp
        {{fileContent}}

Return the fixed code with minimal changes. ";

var response = await _openAI.ChatCompletionAsync(/* ... */); return ParseCodeFix(response); } }

### Phase 4: GitHub Integration (Week 3)

#### Create GitHub Issue

```csharp
// Services/GitHubService.cs
public class GitHubService
{
    private readonly GitHubClient _client;
    
    public async Task<int> CreateIssueAsync(ErrorAnalysis analysis)
    {
        var issue = new NewIssue($"[Auto] {{analysis.ErrorSummary}}")
        {
            Body = FormatIssueBody(analysis),
            Labels = new[] { "bug", "auto-generated", "ai-suggested" }
        };
        
        var created = await _client.Issue.Create("owner", "repo", issue);
        return created.Number;
    }
    
    private string FormatIssueBody(ErrorAnalysis analysis)
    {
        var confidenceValue = analysis.Confidence.ToString();
        var confidenceWithPercent = confidenceValue + "%";
        return $@"
        ## Error Summary
        {{analysis.ErrorSummary}}
        
        ## Root Cause
        {{analysis.RootCause}}
        
        ## Suggested Fix
        {{analysis.SuggestedFix}}
        
        ## Code Changes
        ```csharp
        {{analysis.CodeChanges}}

Confidence

{{confidenceWithPercent}}

---

Auto-generated by AI error analysis "; } }

#### Create Pull Request

```csharp
public async Task<int> CreatePullRequestAsync(ErrorAnalysis analysis, int issueNumber)
{
    // Create branch
    var branchName = $"fix/auto-fix-{{issueNumber}}-{{DateTime.UtcNow:yyyyMMddHHmmss}}";
    await CreateBranchAsync(branchName);
    
    // Apply code changes
    foreach (var change in analysis.CodeChanges)
    {
        await ApplyCodeChangeAsync(change.FilePath, change.OldCode, change.NewCode);
    }
    
    // Commit changes
        await CommitChangesAsync($"Fix: {{analysis.ErrorSummary}}", branchName);
    
    // Create PR
    var pr = new NewPullRequest(
        $"Auto-fix: {{analysis.ErrorSummary}}",
        branchName,
        "main")
    {
        Body = $@"
        ## Auto-Generated Fix
        
        Closes #{issueNumber}
        
        {{analysis.SuggestedFix}}
        
        ## Changes
        {{FormatChanges(analysis.CodeChanges)}}
        
        ---
        *Auto-generated by AI. Please review before merging.*
        "
    };
    
    return await _client.PullRequest.Create("owner", "repo", pr).Number;
}

Phase 5: Complete Workflow (Week 4)

Sentry Webhook Configuration

1. Configure Sentry Webhook:

   • Go to Sentry → Settings → Integrations → Webhooks
   • Add webhook URL: https://api.github.com/repos/owner/repo/dispatches
   • Select events: issue.created, error.created

2. Sentry Alert Rules:

   • Create alert for new errors
   • Trigger webhook on error creation
   • Filter by environment, severity, etc.

Complete Workflow Script

// Workflows/AutoFixWorkflow.cs
public class AutoFixWorkflow
{
    public async Task ExecuteAsync(SentryWebhookEvent evt)
    {
        // 1. Analyze error with AI
        var analysis = await _aiService.AnalyzeErrorAsync(evt.Event);
        
        // 2. Create GitHub issue
        var issueNumber = await _githubService.CreateIssueAsync(analysis);
        
        // 3. If confidence > 70%, generate fix
        if (analysis.Confidence > 70 && analysis.HasFix)
        {
            // 4. Generate code changes
            var codeFix = await _codeFixService.GenerateFixAsync(analysis);
            
            // 5. Create PR
            var prNumber = await _githubService.CreatePullRequestAsync(
                analysis, 
                issueNumber);
            
            // 6. Add comment to issue
            await _githubService.AddCommentAsync(
                issueNumber,
                $"✅ Auto-fix PR created: #{prNumber}");
        }
        else
        {
            // Low confidence - just create issue
            var confidenceText = analysis.Confidence.ToString() + "%";
            await _githubService.AddCommentAsync(
                issueNumber,
                $"⚠️ AI confidence too low ({confidenceText}) for auto-fix. Manual review required.");
        }
    }
}

AI Prompt Engineering

Error Analysis Prompt Template

You are an expert .NET developer analyzing production errors.

Error Details:
- Message: {{error_message}}
- Type: {{error_type}}
- Stack Trace: {{stack_trace}}
- File: {{file_path}}
- Line: {{line_number}}
- Context: {{user_context, request_context}}

Codebase Context:
- Framework: ASP.NET Core 9.0
- Database: PostgreSQL
- Authentication: AWS Cognito
- Cloud: AWS (ECS, RDS)

Analyze this error and provide:
1. Root cause (1-2 sentences)
2. Error summary (brief)
3. Suggested fix (detailed explanation)
4. Code changes (if applicable)
5. Confidence level (0-100%)

Return JSON:
{
  "error_summary": "...",
  "root_cause": "...",
  "suggested_fix": "...",
  "code_changes": [
    {
      "file_path": "...",
      "old_code": "...",
      "new_code": "...",
      "explanation": "..."
    }
  ],
  "confidence": 85
}

Code Fix Prompt Template

Fix this error in the following code:

Error: {{error_summary}}
Root Cause: {{root_cause}}

Current Code:
```csharp
{{current_code}}

Requirements:

• Minimal changes
• Follow existing code style
• Add error handling if needed
• Include comments explaining the fix

Return only the fixed code block.

## Configuration

### Environment Variables

```bash
# Sentry
SENTRY_DSN=https://your-dsn@sentry.io/project-id
SENTRY_ENVIRONMENT=production

# GitHub
GITHUB_TOKEN=ghp_...
GITHUB_OWNER=your-org
GITHUB_REPO=fishinglog-backend

# AI Service
OPENAI_API_KEY=sk-...
OPENAI_MODEL=gpt-4
AI_CONFIDENCE_THRESHOLD=70

# Webhook
SENTRY_WEBHOOK_SECRET=your-webhook-secret

Sentry Alert Rules

Create alerts in Sentry for:

• New errors (any severity)
• High-frequency errors (>10/hour)
• Critical errors (severity: error, fatal)
• Performance issues (slow queries, slow endpoints)

Best Practices

1. Error Filtering

Only auto-fix certain types of errors:

• ✅ Null reference exceptions
• ✅ Validation errors
• ✅ Configuration errors
• ❌ Security issues (manual review)
• ❌ Payment-related errors (manual review)
• ❌ Database connection errors (infrastructure)

2. Confidence Thresholds

• High Confidence (>80%): Auto-create PR
• Medium Confidence (60-80%): Create issue, suggest fix
• Low Confidence (<60%): Create issue only, manual review

3. Code Review

Always require:

• ✅ At least one human review before merge
• ✅ Automated tests must pass
• ✅ Code quality checks pass
• ✅ No security vulnerabilities

4. Monitoring

Track:

• Auto-fix success rate
• AI confidence accuracy
• Time saved vs manual fixes
• False positives

Cost Estimation

Sentry

• Free Tier: 5,000 events/month
• Team Plan: $26/month (50,000 events)
• Business Plan: Custom pricing

OpenAI API

• GPT-4: ~$0.03 per error analysis
• Estimated: 100 errors/month = $3/month
• With code generation: ~$0.10 per fix = $10/month

GitHub Actions

• Free: 2,000 minutes/month
• Estimated: ~5 minutes per error = 200 errors/month free

Total Estimated Cost: ~$40-50/month

Security Considerations

1. Don't auto-fix security issues - Always manual review
2. Validate AI suggestions - Don't blindly apply fixes
3. Limit scope - Only fix specific error types
4. Require reviews - All PRs need human approval
5. Monitor changes - Track what AI is changing

Alternative: Hybrid Approach

Option 1: Sentry + Custom AI Service

Use Sentry for error tracking, build custom AI service:

• More control over AI prompts
• Custom confidence thresholds
• Integration with your workflow

Option 2: Sentry + GitHub Copilot

Use Sentry + GitHub Copilot:

• Sentry creates issues
• GitHub Copilot suggests fixes in PRs
• Less automated, more human-in-the-loop

Option 3: Sentry + Cursor AI

Use Sentry + Cursor AI:

• Sentry creates issues
• Cursor AI analyzes and suggests fixes
• Developer reviews and applies

Next Steps

1. ✅ Set up Sentry (backend, web, mobile)
2. ⬜ Configure Sentry webhooks
3. ⬜ Build AI error analysis service
4. ⬜ Create GitHub integration
5. ⬜ Build code fix generator
6. ⬜ Set up automated workflow
7. ⬜ Test with sample errors
8. ⬜ Monitor and refine

Resources

• Sentry .NET Documentation
• Sentry Webhooks
• GitHub API
• OpenAI API
• Sentry GitHub Integration

---

Last Updated: December 2025
Status: Strategy Document - Ready for Implementation
Recommended: Sentry + OpenAI + GitHub Actions