The term ‘vibe coding’ was coined by Andrej Karpathy in February 2025, when he described it as: “… a new kind of coding I call ‘vibe coding’, where you fully give in to the vibes, embrace exponentials, and forget that the code even exists.”
That may sound like a very vibe-laden explanation, but it speaks directly to the barrier-free future made possible by artificial intelligence (AI). What Karpathy described is more than just faster code generation-it's a fundamental change in how application development is done. Traditional programming involves specifying syntax, functions, data structures, and control flow. Vibe coding flips the focus: The user provides the vision, and an AI agent handles the implementation. Simply put, you don’t have to worry about how to build the app, just what business problem you need to solve.
As stated above, vibe coding introduces a fundamentally different approach to building applications:
In conventional development workflows, everything begins with a blank editor and a detailed plan. Developers are responsible for translating requirements into code, managing systems, and troubleshooting issues directly.
- Define the problem and requirements
Developers first identify what the application needs to do, often breaking it into discrete features and use cases. - Choose a tech stack
This includes selecting languages, frameworks, libraries, and tools best suited for the project’s scope and constraints. - Write code manually
Developers build each component from scratch, implementing logic, data structures, and interface elements one line at a time. - Manage the environment
Setting up and configuring the development environment, version control, build systems, and dependencies is a significant part of the process. - Debug and test
Developers write and execute test cases, resolve bugs, and ensure the system behaves as expected before moving forward. - Integrate and deploy
After testing, code is merged, built, and deployed-often requiring manual steps for configuration and launch.
Instead of handling all the technical steps themselves, users tell an AI agent what they are looking to do or achieve, and the system interprets and executes their intent.
- Describe the problem you need to address
Users start by expressing their goal in plain language-for example, "I need to automate our expense management, can you help me create a solution to do this?” - AI interprets and generates code
The AI tool creates the necessary backend logic, frontend components, and integrations based on the prompt. - Review and test the result
Users run the application, observe its behavior, and determine whether the output matches expectations. - Refine with follow-up prompts
If adjustments are needed, users give updated instructions instead of modifying code directly. - Deploy with minimal setup
Once satisfied, the application is launched-often with one-click deployment-without manual configuration or infrastructure work.
Where conventional coding emphasizes precision in syntax and manual orchestration, vibe coding emphasizes natural-language prompts, high-level feedback, and iterative refinement. The role shifts from writing code line-by-line to guiding AI through conversational feedback and versioning. As such, developers or creators no longer have to think in terms of implementation-their attention shifts to what the application should deliver, not how it is built.
Vibe coding certainly is not the first application of AI in application development. Over the past decade, tools have steadily progressed from basic autocomplete to fully conversational agents capable of building entire applications. The milestones below highlight how quickly this evolution has taken shape:
- 2014: Autocomplete and syntax-aware editors
Code editors like Visual Studio and Sublime Text began offering real-time suggestions and syntax correction, improving speed and reducing common programming errors. - 2017: Early machine learning-based code prediction
Tools such as Kite and TabNine applied machine learning to suggest full lines of code based on context, offering more intelligent support than rule-based autocomplete. - 2020: AI pair programming enters mainstream
GitHub Copilot launched, powered by OpenAI’s Codex model. It introduced a new mode of development where developers could write comments and receive functional code in return. - 2023: Integrated AI-driven workflows
Development platforms started embedding AI into the full software lifecycle, from code generation to testing, UI design, and deployment. These systems moved beyond snippets to generate application frameworks. - 2025: Vibe coding becomes viable
With improvements in large language models (LLMs) and orchestration systems, AI agents now build and refine entire applications based on descriptions. The code itself becomes secondary to the desired outcome.
Whether you are solving a small operational issue or shaping a workflow, vibe coding follows a structured, interactive process:
At its most granular level, vibe coding operates as a loop:
- It begins with a prompt that describes a specific goal in plain language. For example, the instruction might be to create a Python script that reads a CSV file and extracts all email addresses from a particular column.
- The AI then generates the initial code, selecting relevant libraries, writing the function, and incorporating basic error handling.
- The code is executed and the output is reviewed to determine whether it performs as intended.
- Feedback is provided to the AI, often requesting adjustments such as additional error handling, domain-specific filtering, or the creation of unit tests.
- This process is repeated until the result matches the desired behavior.
Speaking more broadly, turning an idea into a deployed application through vibe coding typically follows this sequence:
- The process starts with ideation, where the full scope of the app-including its purpose, key features, user interface, and workflows-is described.
- The AI then generates an initial version of the application, producing the foundational structure, user interface components, data schema, and necessary integrations.
- Once a working prototype is available, further refinement takes place through additional prompts that add, modify, or remove functionality. Top vibe coding solutions guide the process from prototype to production in a way that remains governed, secure, and aligned with enterprise standards.
- The application undergoes testing and validation, where development teams assess its quality, performance, and security posture.
- Finally, the application is deployed, often with a single action, making it immediately accessible to users.
To make vibe coding genuinely effective, consider the following best practices:
- Be precise with prompts
Vague or openended prompts tend to generate generic or misaligned code. Clear instructions lead to fewer iterations. - Give agents one task at a time
Break the project into manageable pieces. AI agents handle smaller contexts more reliably than huge monolithic prompts. - Use checkpoints and version control
Regularly capture stable versions of the app being developed so it can be rolled back if a prompt introduces unintended changes. - Ask clarifying questions
Treat the AI as a collaborator-ask about frameworks, performance tradeoffs, or the logic behind generated code. - Align with organizational maturity
According to research from the ServiceNow Enterprise AI Maturity Index, only a minority of enterprises have clear AIinvestment metrics. Ensuring vibecoding efforts connect to measurable business outcomes is key for longterm success.
Vibe coding exists to make programming more accessible, but it’s not a one-size-fits-all solution. Depending on the use case, technical skill, and desired level of oversight, teams and individuals can adopt different approaches to guide how AI fits into their development process.
In its most exploratory form, vibe coding means handing over almost the entirety of the development process to the AI: you prompt, the AI builds, you launch. This style works well for rapid ideation, personal projects, or weekend prototypes.
Many individuals and teams adopt a middle ground, where parts of the app are generated by the AI, and then humans jump in to refine, tweak, or extend. For example, someone might use an AI agent to scaffold a UI and backend, and then gradually learn coding concepts by modifying the generated code themselves.
For production applications, a more disciplined model may be needed. AI tools serve as collaborators; humans remain focused on architecture, quality, review, and deployment. This is usually called ‘human in the loop’, and to put it another way, you guide the AI, but you also review, test, understand, and own the final product. This ensures that code generated via vibe coding remains maintainable, secure, and aligned to business rules.
Vibe coding is changing who can build software and how quickly they can bring ideas to life and solve business problems/challenges. This provides several clear advantages:
Vibe coding extends the progress made by low-code and no-code platforms, offering even greater flexibility and fewer constraints on who can build apps. Nontechnical creators-entrepreneurs, designers, educators-can seize concepts and transform them into workflows as easily as simply describing what they want the software to accomplish. At the same time, experienced developers gain a new toolset that allows them to shift their focus to solving problems and designing experiences rather than wrestling with syntax and boilerplate.
One of the most tangible advantages of vibe coding is speed. What once might have required weeks or months of development effort can now be prototyped in hours or days. The AI handles lowlevel code, scaffolding, integration, and deployment. This enables organizations to test ideas quickly, experiment with features, and iterate based on feedback.
Because the AI handles much of the technical plumbing, teams can spend more time defining the user experience, exploring unconventional features, and solving business problems. In essence, programmers become strategists, designers, and orchestrators.
As with any new approach, vibe coding is not without potential hurdles. Understanding the following limitations can help teams apply this technology more responsibly:
AIgenerated code may follow patterns that are not optimal for largescale performance or may not adhere to organizational coding standards. Without careful human review, teams may end up with code that is hard to refactor, scale, or maintain down the line.
Generating code rapidly can give rise to vulnerabilities. If code is shipped without proper review, it may skip critical steps: authentication, encryption, input validation, audit logging, etc. Enterprises must remain vigilant about how AIgenerated code handles data, who has access to it, and how it integrates into secure models. Moreover, using external AI services may pose dataprivacy risks if prompts contain proprietary information that may end up getting shared to outside sources. Widespread use of vibe coding could also lead to an explosion of shadow IT (solutions built outside of IT oversight) introducing risk to compliance, security, and system coherence.
When you build an application via AI, you might not fully understand all the underlying code. Over time, as features grow and teams change, this can create a maintenance burden: new developers may struggle to comprehend architecture, dependencies, or logic hidden beneath layers of autogenerated programming. Debugging issues in this environment can be more complex than in traditional manuallywritten codebases.
AI-assisted development is reshaping what software teams look like and how they operate. Vibe coding plays a central role in this shift, especially in how it redefines developer responsibilities.
Vibe coding is not replacing developers-it is reshaping their roles. Engineers move from writing lines of code to defining the ‘what’ and ‘why’ of the application. They focus on architecture, design, integration, and oversight. Senior engineers become stewards of AIdriven development rather than mere implementers.
Team structures, hiring practices and learning pathways are likely to shift. Organizations may prioritize problemsolving, promptcrafting, AIagent oversight and governance over traditional algorithmic expertise. Developers will need competency in AIcollaboration environments and the ability to validate and maintain AIgenerated systems. Education programs may evolve away from syntaxheavy courses toward designthinking, systemorchestration, and prompt engineering.
Vibe coding supports a broader shift-from monolithic development efforts to rapid experimentation, from heavy upfront tech investment to fast iteration and risklight prototyping. Organizations can test ideas quickly, pivot faster, and scale what works. Rather than expensive longrun bets, development becomes a cycle of fast feedback, minimal viable product, refinement, and deployment. This turns ideation into reality with lower sunk costs, higher agility, and a greater focus on outcomes.
ServiceNow makes it possible for organizations to apply vibe coding principles at enterprise scale using AI agents that are contextually aware and fully trained on your organization's specific data, existing configurations, and best practices. Take advantage of ServiceNow Build Agent to generate application logic from natural-language prompts, use UI Builder Agent to create interfaces that connect directly to enterprise workflows, and rely on ATF Troubleshooting Agent to automatically validate and refine functionality as the app evolves. Unlike external tools that operate in isolation, ServiceNow’s agents understand your company’s infrastructure, systems, and compliance needs. These agents integrate deeply with business logic, compliance structures, and cross-functional workflows, making vibe coding not just faster, but operationally viable across the entire enterprise.
Accelerating digital transformation with ServiceNow and vibe coding
By combining ServiceNow’s unified platform with vibe coding workflows, enterprises can significantly cut the time it takes to move from concept to deployment. What starts as a prompt becomes a fully integrated, functional solution-complete with connections to IT, HR, and customer service systems. Build Agent doesn’t generate generic boilerplate; it delivers solutions shaped by your organization’s data. This approach reduces overhead, speeds up experimentation, and enables digital transformation that is grounded in your business processes.
Building enterprise-ready applications with AI assistance
With ServiceNow, humans remain firmly in the loop. Developers and administrators guide the process, review each change, and approve application logic before it goes live. The AI Control Tower monitors AI-generated outputs for security, access, and compliance, providing live oversight and automated remediation. Development takes place within the secure, auditable boundaries of the Now Platform so teams can confidently prototype and launch applications, knowing they meet enterprise standards from the start.
Ready to democratize the coding experience? Demo ServiceNow to see how you can build the applications to power your business.