When you can generate a working automation script just by describing what you want, why wouldn’t you? That’s the promise of vibe coding for IT automation, and it’s spreading fast because it removes the programming barrier. The reality hits when you need to debug a failing workflow, update an integration, or hand off maintenance to someone who didn’t write the original prompts. You’re no longer maintaining code; you’re reverse-engineering decisions that were never actually made.
TLDR:
AI-generated code contains vulnerabilities 45% of the time and produces 1.7x more defects than human code.
Vibe coding creates maintenance nightmares when original creators leave and workflows break.
Code duplication jumped from 8.3% to 12.3% since AI generation became widespread, compounding technical debt.
Visual workflow builders provide transparent automation without security risks or debugging chaos.
Ravenna automates IT workflows through visual builders that any team member can understand and modify.
Understanding Vibe Coding and Its Appeal for IT Automation
Andrej Karpathy introduced the term vibe coding in February 2025, describing a coding approach where developers prompt an LLM (such as ChatGPT) to generate working code from natural language descriptions as part of modern code generation workflows. Instead of manually writing code, you describe what you want the end-state application to do. The AI produces the code, and if it runs without errors, it ships.
For IT automation, the appeal is clear: speed and accessibility. You need a script to provision new user accounts across five systems? Describe the workflow. Want to automate software license reclamation during offboarding? Tell the AI what you need. No programming knowledge required. In this world of IT-workflow-automation-vibe-coding, IT teams already stretched thin can automate repetitive tasks without dedicating weeks to learning Python or JavaScript. What used to require specialized developers now happens in minutes through automated code generation driven by natural language prompts.
But speed comes with costs that most teams discover too late: security flaws, code quality degradation, and mounting maintenance costs.
The Security Nightmare: When 45% of AI Code Contains Vulnerabilities
When you generate code through prompts, you inherit security flaws you can't see. Research testing over 100 AI models found that 45% of AI-generated code contains vulnerabilities, including weaknesses from the OWASP Top 10 list. AI models train on massive code repositories where secure coding practices and insecure patterns sit side-by-side. The model can't distinguish between a well-architected authentication script and one that hardcodes credentials. It learns both patterns equally and reproduces whichever fits the prompt context better.
For IT automation, the stakes escalate quickly:
A script that provisions user access with improper input validation creates an entry point for privilege escalation.
An offboarding workflow that fails to properly revoke API keys leaves orphaned credentials floating in your infrastructure.
Automated backup scripts that log sensitive data expose your organization to compliance violations.
You're not shipping bugs. You're shipping attack vectors that won't surface until someone exploits them.
Code Quality Degradation: AI Generates 1.7x More Issues Than Human Code
AI-generated pull requests contain 10.83 issues on average, compared to 6.45 in human-written code, increasing risk across the software development lifecycle where static analysis and automated testing are often skipped. That's 1.7x more defects per pull request, which means longer code reviews and a higher risk of production failures. But these defects aren't syntax errors that get caught immediately. They're logic errors, poor error handling, and code that works in standard scenarios but fails under edge cases. An AI-generated provisioning script might work perfectly for typical requests, but break when someone has a hyphenated last name or belongs to multiple departments.
For IT automation workflows, these issues compound:
A provisioning script with faulty conditional logic might grant excessive permissions.
An offboarding script with incomplete error handling might fail silently, leaving accounts active after employees leave.
A license reclamation routine with poor structure becomes impossible to audit or modify months later.
The real problem is maintainability. Code without clear structure, consistent naming, or logical organization takes far longer to debug when something breaks. Your team spends more time deciphering what the AI wrote than actually fixing the issue. Human developers make mistakes too, but they write code with future maintenance in mind, adding comments for complex sections and structuring logic for readability.
The Technical Debt Time Bomb: Code Duplication and Maintenance Costs
Code duplication has accelerated since AI code generation became widespread. Between 2021 and 2024, copy/pasted code jumped from 8.3% to 12.3% of total code lines, while refactoring dropped from 25% to under 10%.
The problem, though, is that LLMs don't recognize existing functions or modules. When you prompt for a new workflow, the model generates fresh code even when similar logic exists elsewhere. You end up with five user provisioning scripts that do nearly the same thing with slight variations, each introducing its own hidden dependencies. When you need to change how your system handles department assignments, you have to locate and modify each duplicate individually. That kind of fragmentation can cause big operational headaches, especially when those scripts exist in production applications or environments.
Why These Unseen Issues Can Break IT Automation
Your offboarding workflow contains cloned authentication code. Your access request script has duplicated validation logic. Your license reclamation routine copies the same API calls. A single security patch or integration update now requires changes across dozens of scripts, each needing separate testing and deployment.
Humans typically refactor code to consolidate duplicates and improve structure. AI-generated code never gets this treatment because teams lack the time and visibility to refactor code they didn't write. The debt compounds with every new automation.
Six months in, you have hundreds of scripts with overlapping functionality, inconsistent error handling, and no clear ownership. Changes become expensive and risky. What started as faster delivery has created a brittle system where updates take longer than properly structured code would require.
The Knowledge Transfer Crisis: When the Creator Leaves
When the IT admin who built your access provisioning workflow leaves, your team inherits scripts they can't decipher. Senior software engineers reported 'development hell' when working with AI-generated vibe-code. The approach works for prototyping but creates risks in professional settings where deep understanding matters for debugging, maintenance, and security. The issue is simple: the original creator knew what prompts generated each function and understood the workarounds. When they leave, that knowledge disappears. Your team stares at Python scripts with unclear logic, no comments explaining design choices, and no documentation beyond a prompt buried in a Slack thread from six months ago.
And therein lie the foundational issues with vibe-coding IT workflows:
You can't modify the workflow without risking breaks.
You can't debug failures without understanding the logic.
You can't optimize performance without rewriting from scratch. Critical automations become frozen because touching them carries too much risk.
Human-written code transfers knowledge through structure, naming conventions, comments, and documentation. Vibe-coded scripts transfer confusion and risk.
Missing Business Context: Why AI Cannot Understand Your IT Environment
But there is a much bigger problem with using vibe-coding to generate code that is intended to improve processes in your business, like IT workflows.
LLMs, as a form of artificial intelligence, generate code based on patterns from public repositories and training data. They can't see your company's security policies, compliance frameworks, or existing infrastructure. A model might produce a working password reset script, but it won't know whether your organization requires multi-factor authentication, specific audit logging formats, or integration with your particular identity provider configuration. Your IT environment contains unique constraints. For example:
Compliance teams require specific data retention policies.
Security teams mandate certain encryption standards.
Your tech stack includes custom integrations between systems that require specific API calls in a particular sequence.
AI doesn't know any of this context.
When you prompt for an access provisioning workflow, the AI generates generic code that works in isolation. It doesn't account for your existing role-based access control structure, your approval chains, or your department-specific permission requirements. The script might grant access successfully but violate your security policy by skipping mandatory approval steps or failing to log actions for audit purposes. Business logic also creates additional problems. Your offboarding workflow might need to handle contractors differently than full-time employees, or preserve data for legal holds, or route hardware returns through specific channels based on device value. AI can't infer these rules from a prompt. You get code that handles the happy path but breaks your actual business processes.
Human developers build context through documentation review, conversations with stakeholders, and understanding existing systems. Vibe coding skips this step, producing scripts that work technically but fail operationally.
The Debugging Impossibility: Troubleshooting Code You Didn't Write
Even if you could use an LLM that understood your business, vibe-coding IT automation workflows undermines software development best practices. One of those is being able to troubleshoot issues with code that you didn't write.
When automation scripts fail at 2 AM, you need answers fast. Debugging code you didn't write means understanding the architectural decisions behind it. What authentication method did the developer choose and why? Which edge cases did they account for?
With vibe-coded scripts, those answers don't exist. The AI made no conscious architectural choices. It pattern-matched from training data and generated code that worked during testing. Consider this example: your user provisioning script fails when processing employees with dual department assignments. The error message points to a function handling group membership, but the logic inside doesn't match any standard approach. You can't tell if it's intentional design or an AI hallucination. Fixing it requires reverse-engineering the entire authentication flow.
Integration updates create similar chaos. Your ticketing system updates its API, and three automation scripts break. Each script handles the API differently because each came from separate prompts. You’re debugging three unique implementations that connect to the same system, each with different source code paths and assumptions.
Simple fixes stretch into multi-day investigations because you're simultaneously debugging and learning an architecture that was never actually designed.
Workflow Automation Done Right: The Visual Builder Alternative
Visual workflow builders provide a non-programmatic way to generate code, similar to vibe coding, while avoiding many of its core challenges.
Security: A visual builder allows humans with knowledge of security requirements to stay in the loop through explicit human review and structured review processes. While the visual builder does generate the code, that code is always accessible from the visual builder, allowing technical users to look deeper into the architecture decisions made by non-technical users working with the builder to automate a process.
Degradation: There is no code degradation because the visual builder elements can be based on well known components that have already been vetted.
Maintenance: Because the visual builder canvas that was used to create a specific IT automation workflow is always accessible, any user can update the workflow or components and re-generate out the code base.
Knowledge Transfer: With a visual builder, the architectural decisions made to produce the IT automation workflow are all right there on the canvas. Comments can be included on each component, making sure that future users understand the rationale behind the flow, the components used, and any configuration settings that are included in the process.
Troubleshooting: Instead of unmaintainable Python scripts generated by different users with different LLMS, you see what happens at each step through a drag-and-drop interface.
Ravenna's workflow automation applies this principle to IT operations. When you create an access provisioning workflow, you arrange pre-built, tested nodes that connect to Okta, Google Workspace, or BambooHR. No hidden logic. No security vulnerabilities buried in generated code. Each workflow lives in a visual canvas where any team member can understand the automation at a glance. When your IT admin leaves, workflows don't become frozen assets. The visual structure documents itself. Your team modifies automations without risking system stability or debugging mysterious scripts.
You get the speed of automation without the technical debt, security risks, or maintenance headaches that come with AI-generated code.
Looking At Both Approaches Side-by-Side
The table below shows how each approach, vide coding and using a visual builder, fares on those key challenges.
Criteria | Vibe Coding Approach | Visual Workflow Builder Approach |
Security | 45% of AI-generated code contains vulnerabilities, including OWASP Top 10 weaknesses. Models cannot distinguish between secure and insecure code patterns, creating attack vectors like improper input validation, orphaned API keys, and exposed sensitive data in logs. | Visual builders use pre-built, tested nodes that have already been vetted for security. Human oversight remains in the loop on coding decisions, and technical users can review the generated code architecture to ensure compliance with security requirements. |
Code Quality Degradation | AI-generated pull requests contain 10.83 issues on average compared to 6.45 in human code (1.7x more defects). Issues include logic errors, poor error handling, and code that fails under edge cases. Scripts work in standard scenarios but break with unexpected inputs like hyphenated names or multiple department assignments. | No code degradation occurs because visual builder elements are based on well-known, vetted components. The structured approach prevents logic errors and ensures consistent error handling across all workflows. |
Code Maintenance | Code duplication jumped from 8.3% to 12.3% between 2021-2024 as LLMs generate fresh code instead of recognizing existing functions. A single security patch requires changes across dozens of duplicate scripts, each needing separate testing and deployment. Refactoring dropped below 10%, compounding technical debt. | The visual builder canvas remains accessible for any workflow, allowing users to update components and regenerate the code base. Changes are made once in the visual interface and propagate consistently, eliminating duplicate code and reducing maintenance overhead. |
Knowledge Transfer | When the original creator leaves, teams inherit scripts with unclear logic, no comments, and no documentation beyond buried prompts. Critical automations become frozen because modifications carry too much risk. Senior software engineers reported “development hell” when maintaining projects built with AI coding tools that rely on vibe-coded scripts. | Architectural decisions are visible on the canvas where workflows were created. Comments can be added to each component explaining rationale, flow logic, and configuration settings. Any team member can understand and modify automations without specialized knowledge of the original creator's approach. |
Troubleshooting Generated Scripts | Debugging requires reverse-engineering entire authentication flows and architectural decisions that were never consciously made. Simple fixes stretch into multi-day investigations. Integration updates break multiple scripts, each handling APIs differently because they came from separate prompts. | Visual interface shows what happens at each step through drag-and-drop nodes. Workflows are self-documenting, making it easy to identify where failures occur. Team members can troubleshoot without deciphering mysterious scripts or understanding hidden logic. |
Final Thoughts on Building Sustainable IT Automation
The appeal of vibe coding disappears when you calculate the true cost of unmaintainable scripts and security vulnerabilities. IT automation risks multiply when your workflows become frozen assets that nobody can safely modify or debug. You can automate repetitive tasks without creating technical debt that costs more to fix than the time you saved. Choose transparency over speed that breaks your infrastructure.
FAQs
What makes vibe coding risky for IT automation workflows?
Vibe coding produces scripts that work initially but contain hidden security vulnerabilities (45% of AI-generated code has exploitable flaws), create 1.7x more defects than human-written code, and generate unmaintainable duplicates that become impossible to debug when the original creator leaves your team.
How does visual workflow automation differ from AI-generated scripts?
Visual workflow builders use pre-built, tested nodes in a drag-and-drop interface that any team member can understand and modify, while AI-generated scripts create hidden logic with security risks and maintenance problems that require reverse-engineering to troubleshoot.
Can AI-generated code understand my company's specific security policies?
No. LLMs generate code based on public repositories and can't account for your organization's unique compliance requirements, approval chains, role-based access controls, or integration sequences between your specific systems.
What happens when the person who created vibe-coded automations leaves?
Your team inherits scripts with unclear logic, no documentation beyond buried prompts, and no comments explaining design choices, making modifications risky and debugging nearly impossible without rewriting everything from scratch.
Why does AI-generated code create more technical debt over time?
AI doesn't recognize existing functions, so it generates duplicate code for similar tasks (copy/pasted code jumped from 8.3% to 12.3% between 2021-2024), while refactoring dropped below 10%, meaning a single security patch now requires changes across dozens of separate scripts.
