Using Execution Data for AI and Auto-Remediation

🎁  Download Part 1 Here: https://go.n8n.io/n8n-dashboard-frontend
🎁  Download Part 2 Here: https://go.n8n.io/n8n-dashboard-backend

In this episode, Aemal walks through building a comprehensive monitoring dashboard for n8n workflows that enterprises can use to track every execution, identify failures, and even calculate workflow costs.

Timestamps:
0:00 - Introduction
2:30 - Understanding n8n Backend Hooks
8:45 - Setting Up Supabase Schema
15:20 - Capturing Execution Data
22:10 - Building the Dashboard with Replit
35:15 - Security Best Practices
38:40 - Future of AI Development

What You'll Learn:
• How to use n8n backend hooks to capture execution telemetry data
• Setting up Supabase to store workflow execution logs
• Building a real-time dashboard with Next.js and Replit
• Implementing enterprise-grade monitoring for n8n instances
• Security best practices when connecting frontend to backend
• Using AG Grid for advanced data filtering and visualization

Key Features Built:
✅ Real-time execution tracking
✅ Failure monitoring and notifications
✅ Execution data analysis with full JSON context
✅ Cost tracking for workflows (especially token usage)
✅ Enterprise-grade UI with Hero UI components

Tech Stack:

n8n (workflow automation)
Supabase (database)
Next.js (full-stack framework)
Replit (development & deployment)
AG Grid (data tables)
Hero UI (component library)
GitHub Repositories:
Both the n8n backend hooks and the dashboard UI are available as open-source repositories. Links in the description!

Perfect For:

CTOs and technical leaders monitoring n8n deployments
Business operators tracking workflow performance
DevOps teams needing execution visibility
Anyone running enterprise n8n instances
Bonus Discussion:
We also dive into the future of AI-native development, vibe coding with tools like Replit and Cursor, and how AI is transforming software engineering productivity by 20-50x.

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