There's no denying that AI's ability to quickly write code is appealing. This method has been called "vibe coding," which means a free-flowing, experimental style in which prompts are thrown at an AI, and the code that comes out is put together with little oversight. This method may seem like a way to get more done, especially on projects that are still in their early stages, but it is quickly becoming a major problem in production settings. Hackathons are too chaotic to produce software that is essential for a mission. Putting speed ahead of structure in your sprints will lead to regressions, codebases that are easy to break, and huge security holes.

This trend isn't a one-time thing; it's a logical result of too much excitement about AI-assisted development.

From Vibe Coding to Vibe Engineering: A Change That Had to Happen

We shouldn't give up on AI completely; instead, we should change the way we use it. "Vibe engineering" is a new way of thinking that uses AI's ability to create things while keeping control, intent, and strict limits. In this model, developers go from being coders to being architects. They set clear limits, manage specialized AI agents, and define how the system should work. The focus changes from just writing code to carefully designing strong software.

The AI is no longer an independent being all of a sudden. It's working within set limits, based on the information it has, and is responsible for following clear instructions.

Vibe engineering uses smart agents that can look at your repository, determine the structure of your system, and utilize existing components intelligently. The resulting code not only functions effectively, but it also fits in perfectly. Testing now forms an integral part of the process, automatically applying principles that prioritize safety and consistently follow established coding patterns.

What happened? Code that is ready for PR right away, easy to understand, and easy to add to. It helps create a software development lifecycle that can grow without making things unstable.

The main idea behind vibe engineering is to go from reacting and improvising to proactively orchestrating. You're not just writing lines of code; you're also designing the system that makes your software, checks it, and keeps it safe in the future.

The Developer's Changing Role: Accepting Orchestration

To succeed in this new job as an orchestrator, you should think about following these four important steps:

Don't just think about the task; think about the ecosystem. Please consider not just completing one task, but also thoughtfully evaluating which parts of your larger system need We need to make changes to the system in a clean and long-lasting manner. Does the modification require a new abstraction layer, an update to a shared utility, or a change to the way a core service works? This could involve updating a utility or modifying the way a core service operates. Instead of just fixing one thing, think about how to improve the whole system. Before you start coding, ask yourself, "What would make it easier to add this new feature?" Add reducing technical debt or making abstraction better to the feature delivery pipeline.

Please document your guidelines and set boundaries for the AI. Please ensure your AI assistant is familiar with the architectural rules, style guidelines, and workflow expectations before it begins writing code. This includes rules for naming things, preferred directory structures, set abstraction boundaries, and areas of your codebase that are off-limits. Some tools can figure out these rules on their own, but it's always helpful to have clear instructions. Clearly stating these expectations allows the AI to code in a manner consistent with what a seasoned team member would do. Transform your current code standards into clear rules that the AI can follow, either as formal rules or as documentation that is often used.

Encourage reuse and improvement: Instruct the AI to focus on the current and robust sections of your codebase, intentionally excluding any outdated or deprecated code. Focus on using already-made reusable parts, utility functions, and services that can be added to. The goal is to naturally grow the codebase from its best parts while following the DRY (Don't Repeat Yourself) rules. Some platforms automatically find chances to reuse code, while others depend on the context that developers give them. No matter what, the AI will either make the system more or less secure based on your instructions. Find the official Locate the official source code for shared logic and make sure your prompts reference it. For example, you might say, "If this utility seems old or broken, refactor it to fit with X" to get the AI to suggest ways to improve it when you use old code again.

Please ensure consensus is reached by implementing test-driven development (TDD). Because AI can easily make tests, use test-first workflows as your default. This approach finds problems before they happen, before implementation starts. You can find hidden assumptions, inconsistencies, or unclear logic early in the process by telling the AI to first make tests based on how you want it to behave. Before you write any functional code, ask the AI to make tests that show how the feature should work. Carefully look over the tests that were made; they show how the AI understood what you wanted. Before you start coding, make sure to fix or change anything that doesn't add up. Make "test first, then build" a standard way of doing things.

Stay aware of the codebase: Use AI-powered tools to summarize and monitor pull requests and track how the codebase evolves. Then, make sure to actively read those summaries. AI agents always know about code changes in real time, but human developers don't. You need to put in the work to stay aware of architecture, especially as AI speeds up the development process. To make smart choices and stop code drift, you need to know how changes will affect the whole system. Please consider subscribing to summaries of merged pull requests and setting them aside. time every day to read them.

These strategies make up the modern software orchestrator: a professional who thinks about the whole system, makes their goals clear, treats AI as a partner, and puts future-proof design first.

The urge to go fast is strong. It's nice to be able to drop a prompt, get executable code, and deploy it. This method will always fail when correctness, maintainability, and scalability are the most important things. Teams end up fixing code that looked like it would work at first but breaks down under stress.

Understanding the distinction between writing code that functions and creating software that endures is crucial. Anyone can write code that compiles, but making something strong requires planning, testing, and being intentional.

The Short-Lived High of Vibe Coding

At the beginning of a project, vibe coding can be very exciting. It's easy to use quick prompts and outputs that are "good enough" for a demo. This is especially true when there is no legacy code to deal with, no existing test suite to break, and no complicated edge cases to consider for a demo.

But this approach is a trap.

Picture a programmer adding a new feature. It compiles and works fine when everything is perfect. The team deploys the feature. A week later, a bug report comes in, saying that users are getting the generated code that fails to perform the necessary authorization checks. No tests were conducted to identify the error. Because the code "looked right" on the surface, a quick review of it didn't find the problem.

As the team looks into it, more problems come up, like inconsistent naming conventions, business logic mixed up with the UI code, and components that can be used again being rewritten from scratch. One bug fix causes another bug. The feature and the AI-assisted workflow begin to lose their credibility. The initial gains in productivity are quickly wiped out by rising technical debt and the frustrating job of fixing a poorly designed system. The allure of vibe coding may entice you, but it often leaves you stranded by mounting technical debt.

Williams Bank

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