AI is changing how software is built, but it has not made software engineering obsolete. The real risk is not that AI writes code. It is that we mistake working code for well-engineered software.
Criticism of vibe coding is becoming increasingly common. The concern is understandable: someone can ask a coding agent to build an application, see it running minutes later, and assume the job is done. Yet a functioning interface says little about the architecture beneath it, its security, maintainability, or behavior under real-world conditions.
The distinction matters. Asking AI to build an application without knowing how to engineer one is not the same as using AI to engineer software.
The problem is not who writes the code
Software quality has never depended solely on the person typing the code. It depends on requirements, architectural decisions, implementation standards, testing, review, and the ability to identify problems before they reach production.
Without these foundations, AI can accelerate the creation of fragile systems. With them, it can accelerate disciplined development, and make deeper verification practical within the same delivery effort.
In my experience, the greatest benefit of AI-assisted development is not simply generating code faster. It is being able to spend more effort on design, anticipate weaknesses earlier, and build stronger automated checks without making every improvement prohibitively expensive.
Control does not mean reading every line
A frequent objection is that developers lose control over the countless small decisions an AI makes: whether to create one method or two, use a conditional or another strategy, or organize an implementation in a particular way.
That concern deserves attention, but it also raises a question: how is control exercised in conventional software teams?
A software architect does not ordinarily prescribe every condition or inspect every line written across a large project. Instead, the architect establishes boundaries, design principles, quality expectations, and review practices. Teams use automated tests, static analysis, security scanning, performance checks, and targeted code reviews to detect deviations and risks.
Software engineering has never been about personally controlling every line of code. It is about controlling the process that produces and validates it.
The same principle applies to AI-assisted development. Faster code generation increases the need for effective controls, but AI also makes it faster to create test infrastructure, enforce standards, and investigate findings. Human review remains essential, especially for critical logic, security-sensitive changes, and architectural decisions. Automated gates cannot prove that every design choice is correct; they make verification more systematic and help direct attention where judgment matters most.
From Vibe Coding to an engineering process
My approach follows four connected stages. AI contributes throughout, but the engineering responsibility stays with the people directing the work.
1. Design the solution before writing code
I begin with architecture: requirements, responsibilities, component boundaries, data flows, integrations, security, and likely failure points. AI is a useful discussion partner at this stage. I use it to challenge assumptions, explore alternatives, and refine the design before instructing a coding agent to implement anything.
The result is a documented description of the intended solution, not merely a prompt asking for an application.
2. Turn the design into a technical plan
The architectural description becomes the input to a separate planning session. Here, the goal is to translate the solution into components, implementation rules, dependencies, and ordered phases: from the initial solution structure to successive functional layers.
Separating design from implementation planning reduces ambiguity and makes the work easier to evaluate.
3. Implement with quality and security gates
The coding agent implements the planned phases while building the supporting verification infrastructure: automated tests, static analysis, dependency and security checks, and other quality gates appropriate to the project.
These checks are not an afterthought. They are part of the delivery process, with failures investigated before code is accepted or released. Coverage numbers and passing tests alone are not proof of quality; the checks must address meaningful risks and expected behavior.
4. Review, challenge, and refine
Review happens during implementation and again at defined version milestones. More capable AI models can help examine the broader codebase for inconsistencies, duplication, architectural drift, and missed risks. Their findings must still be verified, and critical changes deserve human scrutiny.
This creates an iterative cycle: implement, test, review, correct, and reassess, not a one-shot generation exercise.
The engineer's role is evolving, not disappearing
Vibe coding and AI-assisted software engineering may use similar tools, but they do not apply the same discipline. One can prioritize obtaining a visible result; the other treats that result as only one part of delivering reliable software.
AI does not remove the need to understand architecture, security, testing, or maintainability. It raises the value of those skills because a competent engineer can now apply them across a much faster development process.
The opportunity is not to surrender control to AI. It is to use AI to build software, and the mechanisms that keep that software under control, more effectively.