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Vibe coding changed how developers write software. Now, AI coding agents are changing how software itself gets built.
September 23, 2026
For the past few years, AI has been steadily changing the way software is written. Developers started with autocomplete tools, then moved to AI assistants that could explain code, generate functions and help debug errors. Then came vibe coding.
The basic idea behind vibe coding is simple. Instead of manually writing every line of code, a developer describes what they want in natural language and lets an AI model generate much of the implementation.
It made software development feel less like typing instructions and more like communicating an idea.
But there is a bigger shift happening now. AI is moving beyond generating individual pieces of code. Modern coding agents can increasingly understand an entire repository, plan tasks, modify multiple files, run commands, execute tests and iterate on their work.
This is where the idea of AI-native software development starts becoming important.
The future of development may not simply be about humans writing code faster with AI. It may be about humans designing software workflows in which AI performs a large part of the implementation process.
There is an important difference between using AI as a coding assistant and building software in an AI-native way.
In traditional software development, the developer writes most of the code and controls the implementation directly.
With AI-assisted development, the developer remains in control but uses AI for suggestions, explanations, code generation and debugging.
Vibe coding goes a step further. The developer describes the desired result and AI generates a substantial portion of the implementation.
AI-native development goes beyond individual prompts. The entire workflow can be designed around AI agents that receive a task, inspect the project, create a plan, implement changes, run tests and report the results.
A simple way to understand this progression is:
The biggest change is not simply that AI can write more code. The bigger change is that AI can participate in more stages of the development lifecycle.
Early coding assistants were mainly useful for completing lines or generating small functions. Modern coding agents can work with larger repositories and make coordinated changes across multiple files.
For example, a developer could ask an agent to add a new feature. The agent may need to update the backend, modify database models, create API endpoints, change the frontend and add tests.
Instead of generating each piece separately, the agent can approach the request as one software task.
This changes the unit of interaction.
Instead of saying, "Write this Python function," developers can increasingly give instructions such as, "Add authentication to this application, update the database schema, create the required API endpoints and add tests."
The AI then needs to break that larger request into smaller technical steps.
Real software development involves much more than writing code. Developers inspect files, search documentation, run commands, execute tests, analyse errors and manage version control.
AI agents are increasingly being connected to these tools. This allows them to perform actions instead of simply responding with text.
That distinction is crucial. A chatbot can tell you how to fix a failing test. An agent can potentially inspect the failure, modify the code, run the test again and continue iterating.
Another emerging pattern is parallel development with multiple AI agents.
One agent might work on a backend feature while another handles tests and another investigates a bug. A human developer can then review the results and decide which changes should be accepted.
This changes the role of the developer from manually executing every task to coordinating and reviewing several streams of work.
If AI can write, test and modify large amounts of code, does that make developers less important?
A more useful question is what happens when implementation becomes cheaper.
Developers still need to understand requirements, architecture, system design, security, performance, databases and business constraints. They also need to determine whether the AI-generated implementation is actually correct.
In an AI-native workflow, the developer increasingly becomes an orchestrator, reviewer and decision-maker.
This makes skills such as the following increasingly valuable:
In other words, knowing how to produce code is becoming only one part of being a strong software engineer.
Vibe coding is extremely useful for prototypes, experiments and quickly turning an idea into something functional. But production software introduces another set of requirements.
A project can work perfectly during a quick demo and still become difficult to maintain later. AI-generated code can contain bugs, security issues, unnecessary complexity or assumptions that the developer did not notice.
There is also a visibility problem. When developers accept large amounts of generated code without understanding it, they may struggle to explain why a system behaves in a particular way.
This becomes especially important when the software handles sensitive information, financial transactions, authentication or other critical workflows.
That is why the future is unlikely to be simply "AI writes everything and humans stop coding."
A more realistic direction is AI handling more implementation while humans remain responsible for specifications, validation, architecture and accountability.
Imagine building a SaaS application using an AI-native workflow.
Instead of spending hours moving between an editor, terminal, documentation and browser tabs, you could give an AI agent a structured task:
"Add a subscription system. Use the existing user model, create the required database changes, integrate the payment provider, add error handling, write unit and integration tests, and prepare a pull request."
The agent could inspect the repository, identify relevant files, create a plan, implement the changes, run tests and report problems.
The developer's job would then be to review the plan and result, challenge assumptions and decide whether the implementation should be accepted.
This is much closer to delegation than traditional autocomplete.
The important question therefore changes from "Can AI write this function?" to "Can AI reliably complete this software task under the constraints I provide?"
One of the biggest questions around AI-native development is whether beginners should still learn programming deeply.
The answer is yes, but the reason is changing.
You may not need to manually write every piece of boilerplate code. But you still need enough technical understanding to recognise when an AI-generated solution is wrong.
Consider debugging. If an AI agent tells you that a database query is correct but your application is accidentally exposing sensitive records, accepting the generated code without understanding it can create serious problems.
Understanding SQL, databases, authentication and access control gives you the ability to question the output.
The same principle applies to Python, JavaScript, APIs, cloud infrastructure and machine learning.
AI can reduce the amount of code you personally type. It does not eliminate the value of understanding what that code is supposed to do.
AI-native development is also creating a new layer in the software development stack.
Developers are increasingly working with more than programming languages, frameworks and databases. They also need to understand how AI systems interact with software projects.
This is why AI-native software engineering is becoming more than another form of autocomplete. It is becoming a different way of organising development work.
The most important change may not be developer productivity alone. It may be what becomes possible when software implementation can be delegated more effectively.
A small team could experiment with more product ideas. Developers could build internal tools faster. Teams could spend more time thinking about product requirements and architecture instead of repetitive implementation.
There is also a potential shift in who can build software. People with strong domain knowledge but limited programming experience may be able to work with AI systems to create useful prototypes and internal applications.
However, turning a prototype into reliable production software still requires engineering discipline.
The interesting future is therefore not one where humans disappear from software development. It is one where humans spend more of their time deciding what should be built and how it should be evaluated, while AI handles an increasing amount of the implementation work.
If you are learning software development today, it can be tempting to focus entirely on prompt engineering or the latest AI coding tool.
That approach can become outdated quickly because AI tools are changing rapidly.
A stronger strategy is to combine programming fundamentals with AI-native workflows.
The goal should not be to compete with AI at typing code.
The goal should be to become better at defining problems, designing systems and directing AI toward reliable solutions.
Vibe coding introduced a simple but powerful idea: developers do not always need to express every software instruction manually.
AI-native development takes that idea further. The unit of work is gradually moving from individual code snippets toward complete tasks, features and workflows.
That does not mean traditional programming disappears. Instead, programming knowledge becomes part of a larger skill set that includes system thinking, specification, evaluation and AI orchestration.
The developer of the future may spend less time asking, "How do I write this code?" and more time asking, "What exactly should this system do, how should it behave, and how can I verify that the AI built it correctly?"
That is the real shift beyond vibe coding.
AI-native software development is an approach where AI agents are integrated into the core software development workflow. Instead of using AI only for code suggestions, developers can delegate tasks involving planning, implementation, testing, debugging and documentation.
Not exactly. Vibe coding generally focuses on describing desired software outcomes in natural language and allowing AI to generate the implementation. AI-native development is broader and can involve autonomous agents, structured specifications, tools, testing, repositories and multi-step development workflows.
AI is changing software development roles, but its impact depends on the type of work, organisation and level of human oversight. Developers still need to handle requirements, architecture, security, validation and technical decision-making.
Yes. Programming fundamentals help developers understand, evaluate and debug AI-generated software. As AI handles more implementation, understanding systems and validating their behaviour becomes increasingly important.
Programming fundamentals, software architecture, debugging, testing, Git, system design, security and the ability to work effectively with AI agents are all becoming useful. Clear problem definition and technical judgement are particularly important because AI systems depend heavily on the context and requirements they receive.
Vibe coding changed the relationship between humans and code. AI-native software development is changing the relationship between humans and the entire development process.
The next generation of developers may not spend most of their time manually producing code. They may spend more time designing systems, defining requirements, coordinating AI agents, reviewing implementations and ensuring that software behaves correctly.
The important skill is therefore not simply learning how to make AI write code. It is learning how to build software effectively when AI can participate in almost every stage of the process.
The era after vibe coding is not necessarily the end of coding. It is the beginning of a development workflow where humans increasingly describe, direct, evaluate and orchestrate software while AI handles more of the implementation.
Anas Khan
Technology and data science writer focused on making complex technical topics easier to understand.