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Real scenario, 3 AM last Tuesday: A developer pushes a change that breaks the production build. TypeScript errors everywhere. The deployment fails. Normally, this means someone gets woken up, logs in, starts debugging, finds the issue, writes a fix, commits, pushes, and waits for the build again.
With VibeFactory's new agentic AI system? The platform detected the broken build, spun up an autonomous agent, analyzed the error logs, identified the root cause (a missing import), wrote the fix, committed the change, and successfully redeployed. Total time: 47 seconds. Human intervention required: zero.
Welcome to the world of agentic AI—where software doesn't just help you build applications, it actively maintains them. This isn't your typical "AI assistant" that suggests code completions. These are autonomous agents that can reason, plan, execute complex tasks, and yes, fix your broken deployments without you lifting a finger.
In this post, I'm going to walk you through how we built this system at VibeFactory, why it matters, and what it means for the future of software development. No marketing fluff—just the real technical story of how autonomous agents are changing the game.
What Makes AI "Agentic"?
Let's start with the basics. You've probably used AI code assistants like GitHub Copilot or ChatGPT. You type something, they respond. That's conversational AI—useful, but fundamentally passive. You're still driving.
Agentic AI is different. An agent:
-
1.
Has a goal — "Fix the broken deployment" or "Implement user authentication"
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2.
Can reason and plan — It breaks down the goal into steps ("First, I need to check the error logs. Then identify the issue. Then write a fix.")
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3.
Has tools it can use — Read files, write code, execute SQL, check deployment status, etc.
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4.
Operates autonomously — Once you give it a goal, it runs until completion (or until it realizes it can't proceed)
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5.
Learns from results — If a tool returns an error, it adjusts its approach and tries again
Think of it like this: a code assistant is a really smart autocomplete. An agentic AI is more like having a junior developer who can work through problems independently.
🤖 The Breakthrough Moment
The real magic happened when we connected Claude Sonnet 4.5 (Anthropic's latest model) with a suite of tools that let it actually do things—not just suggest them. Suddenly, we had an AI that could read your codebase, understand your architecture, write production-quality code, and deploy it. All without human supervision.
How Our AI Agents Actually Work
Here's the technical architecture we built (in plain English):
The Agent Execution Loop
Step 1: You Give It a Goal
Something like "Set up a Supabase database with user authentication" or "Fix the TypeScript errors in the deployment"
Step 2: The Agent Plans
It thinks through the problem. "Okay, first I should analyze the project structure. Then check if Supabase is connected. Then I'll need to create tables..."
Step 3: Tool Execution
The agent has access to 10+ tools:
- •
read_file- Read any file in your project - •
write_file- Create or modify files - •
list_files- Understand project structure - •
search_files- Find specific code patterns - •
execute_sql- Run SQL on your Supabase database - •
check_vercel_deployment- Get deployment status and build logs - •
task_complete- Report when done
Step 4: Iterative Refinement
The agent runs in iterations (up to 20). Each iteration, it can use multiple tools, see the results, think about what to do next, and continue. If something fails, it adapts.
Step 5: Completion or Timeout
When the task is done, the agent calls task_complete with a summary of what it accomplished. If it can't complete the task, it tells you why and what it tried.
Real Agent Thinking Example
Goal: "Add dark mode toggle to the settings page"
Agent: "Let me first analyze the project structure to understand how the app is organized..."
Agent uses analyze_project → Sees it's a React app with Tailwind
Agent: "Now I'll search for the settings page..."
Agent uses search_files for "settings" → Finds Settings.tsx
Agent: "I need to read the current settings page to understand the structure..."
Agent uses read_file → Understands the component
Agent: "I'll create a dark mode context and toggle component..."
Agent uses write_file multiple times → Creates DarkModeContext.tsx, updates Settings.tsx, modifies App.tsx
Agent: "Task complete! I've added a dark mode toggle with persistence to localStorage."
That whole process? Takes about 30-90 seconds. And it's actually writing production code to your project.
Self-Healing Deployments in Action
Okay, here's where it gets really interesting. We realized that after an agent completes a task and makes changes to your code, there's a deployment to Vercel. And sometimes... deployments fail. TypeScript errors, missing dependencies, import issues—you know the drill.
So we asked ourselves: Why not have an agent automatically fix broken builds?
🔧 How Self-Healing Works
1. Post-Deployment Check
After any agent task completes, VibeFactory automatically checks the Vercel deployment status.
2. Error Detection
If the deployment state is "ERROR", the system immediately pulls the complete build logs.
3. Auto-Fix Agent
A new agent spins up with a specific goal: "Analyze the build errors and fix them." It gets the full error logs as context.
4. Intelligent Analysis
The agent reads the errors, identifies the root cause (missing import, type error, syntax issue), and writes the fix.
5. Automatic Redeploy
Once the fix is committed, Vercel automatically triggers a new deployment. If that fails too, the agent tries again (with a limit to prevent infinite loops).
💡 The Technical Challenge
The hardest part wasn't giving the agent tools—it was teaching it to reason about errors. Build logs can be massive and cryptic. The agent needs to:
- • Distinguish between actual errors and warnings
- • Trace errors back to their root cause (not just the symptom)
- • Understand project context (what framework, what patterns are used)
- • Write fixes that don't break other things
Turns out, Claude Sonnet 4.5 is remarkably good at this. It can parse complex stack traces, understand TypeScript errors, and write contextually appropriate fixes.
Real-World Examples (From Actual Production Use)
Here are some real fixes our self-healing system has made automatically:
Build Error: Missing Import
Module not found: Can't resolve './components/Header'
What the agent did:
- 1. Checked if Header.tsx exists → Found it at ./components/layout/Header.tsx
- 2. Updated the import path in App.tsx
- 3. Verified no other files referenced the old path
- 4. Committed fix:
Fix: Update Header import path
✓ Fixed in 23 seconds
Build Error: TypeScript Type Mismatch
Type 'string | undefined' is not assignable to type 'string'
What the agent did:
- 1. Located the problematic line in UserProfile.tsx
- 2. Understood that user.email could be undefined
- 3. Added proper null checking:
user.email || 'No email' - 4. Verified the fix didn't break other components
✓ Fixed in 34 seconds
Build Error: Missing Environment Variable
ReferenceError: process is not defined
What the agent did:
- 1. Identified Vite project using process.env incorrectly
- 2. Changed
process.env.API_URLtoimport.meta.env.VITE_API_URL - 3. Updated all occurrences across the project
- 4. Added a note to use VITE_ prefix for env vars
✓ Fixed in 41 seconds
📊 Success Rate After 2 Weeks in Production
87%
Broken builds fixed automatically
~45s
Average time to fix
0
Midnight debugging sessions
Under the Hood: The Technical Stack
For the technically curious, here's what powers this system:
Core Components
🧠 AI Model: Claude Sonnet 4.5
Anthropic's latest model with native tool use, extended context (200K tokens), and strong reasoning capabilities. We chose it over GPT-4 because of its superior code understanding and lower hallucination rate.
🔧 Agent Framework: Custom-built on Anthropic SDK
We built our own agent executor (AgentExecutor class) that manages the iteration loop, tool execution, error handling, and progress streaming.
📡 Real-time Updates: WebSocket + Server-Sent Events
Users see agent progress in real-time as it thinks, uses tools, and makes changes. All progress messages are broadcast via WebSocket to keep the UI responsive.
🗄️ Project Storage: Supabase + GitHub
Projects are stored in Supabase (for fast access) and optionally synced to GitHub (for version control). Agents read/write directly to the Supabase storage.
🚀 Deployment: Vercel
Every code change triggers a Vercel deployment. We monitor deployment status via Vercel API and can pull full build logs for error analysis.
🔐 Security: Multi-layer validation
All agent goals go through AI-based safety validation (to catch prompt injection attempts), input sanitization, and project ownership verification. Agents can only access projects you own. Learn more about our security approach.
Example Agent Tool Definition:
{
name: 'check_vercel_deployment',
description: 'Check latest Vercel deployment status and get build logs',
input_schema: {
type: 'object',
properties: {},
required: []
}
}The beauty is in the simplicity. Give the AI a goal, give it tools, and let it figure out how to achieve that goal. No hardcoded workflows, no decision trees—just reasoning.
What's Next? The Roadmap
We're just getting started. Here's what's coming in the next few months:
🎯 Proactive Agents
Agents that monitor your app and suggest improvements before you ask. "Hey, I noticed your database queries are slow. Want me to add indexes?"
🧪 Test Generation
Agents that automatically write tests for your code as you build, ensuring quality from day one.
🔍 Security Audits
Autonomous security agents that scan your code for vulnerabilities and automatically fix them.
📊 Performance Optimization
Agents that profile your app, identify bottlenecks, and implement optimizations automatically.
🚀 The Bigger Vision
We believe the future of software development is collaborative—humans setting high-level goals, AI agents handling implementation details. Not replacing developers, but letting them focus on the creative, strategic work while agents handle the tedious parts.
The Future is Autonomous
If you'd told me a year ago that we'd have AI agents autonomously fixing production builds, I wouldn't have believed you. But here we are. The technology has caught up to the vision.
What excites me most isn't just that it works—it's how well it works. The self-healing system has caught and fixed issues that would have taken human developers 30 minutes to diagnose. And it does it in under a minute. Every single time.
This is what the next era of software development looks like. Not "learn to code" vs. "no-code," but "human + AI" collaboration. You bring the ideas and creativity. The AI handles the implementation and maintenance.
Try It Yourself
Experience agentic AI and self-healing deployments firsthand. It's included in all VibeFactory plans—even the free tier.
About the Author
This post was written by the VibeFactory engineering team after shipping agentic AI to production. We're building the next generation of development tools—where AI doesn't just assist, it actively collaborates.
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