A few years ago, AI coding mainly meant asking ChatGPT to write a function, explain an error, or generate a small piece of code.

Today, the situation is different.

AI coding agents can work with large codebases, inspect files, use tools, write code, run tests and continue working through a task.

This is becoming one of the biggest changes in software development.

Recently, Google introduced Gemini 3.7 Flash with a strong focus on coding and agent workflows, while Meta also introduced Muse Code, an AI coding agent designed to work with large codebases.

So the important question for developers is no longer:

"Can AI write code?"

We already know the answer is yes.

The more important question is:

"How should developers work when AI can handle a large part of the coding process?"

From AI Assistant to AI Coding Agent

There is a big difference between an AI assistant and an AI coding agent.

A traditional AI assistant works something like this:

For example:

"How can I create a Laravel queue job?"

The AI gives you an answer.

You then implement it.

An AI coding agent works differently:

The agent is not just generating code.

It is attempting to complete a development task.

That is a major difference.

Why This Change Is Important

Imagine I have a Laravel application with:

Suppose I want to add:

"Allow customers to download their monthly invoice as PDF."

A simple AI assistant might generate a controller.

But a coding agent can potentially inspect:

Then it can make changes across multiple files.

This is much closer to real software development.

Large Codebases Are the Real Test

Generating a 50-line example is not very difficult anymore.

The real challenge is understanding an existing application.

A real project may contain:

An AI agent needs to understand the relationships between these components.

This is why large-codebase coding agents are becoming important.

Meta's Muse Code, for example, is positioned specifically around handling complex software and large codebases.

What Does This Mean for Laravel Developers?

As a Laravel developer, I see many practical use cases.

Suppose I have:

An AI coding agent can potentially work across this entire stack.

For example:

"Add a customer import system with CSV upload, background processing, validation errors and an import history page."

This is not one coding problem.

It involves:

This is exactly the type of multi-step work where coding agents become interesting.

AI Can Handle More Boilerplate

There are many things developers write repeatedly.

For example:

AI is very good at generating this kind of repetitive code.

Instead of spending an hour creating basic CRUD, I can potentially spend a few minutes describing the requirements and then review what the agent generated.

This does not mean the developer becomes unnecessary.

It means the developer's time can move toward higher-value work.

The Developer's Job Is Changing

I think this is one of the most important changes.

Earlier, developers spent a lot of time on:

Increasingly, developers will spend more time on:

Recent industry discussions around agentic coding are also pointing toward this shift, where implementation becomes more automated while human judgment and software architecture become more important.

AI Does Not Understand Your Business Automatically

This is where experienced developers still have a major advantage.

Imagine an ERP system.

A developer knows:

An AI agent may understand the code structure.

But it may not understand why a particular invoice series cannot be changed after an invoice is generated.

That is business knowledge.

The developer needs to provide this context.

So I believe experienced developers will become more valuable when they learn how to give AI the right context.

Context Is Becoming a Developer Skill

With AI coding agents, the quality of the result depends heavily on the context.

Instead of saying:

"Create an invoice module."

I would provide:

Then the agent has a much better chance of producing code that actually fits the application.

So developers should learn:

How to give AI the right context.

This is becoming almost as important as writing a good prompt.

AI Agents Can Work With Tools

A coding agent becomes much more powerful when it has access to tools.

For example:

Now the AI can inspect the application instead of guessing.

This is also where technologies such as MCP become important.

MCP provides a standardized way for AI applications to interact with tools and resources.

The broader ecosystem is also moving toward standardized agent communication, with Google's A2A protocol recently moving into the Agentic AI Foundation to encourage interoperability between independent agents.

Coding Agents Can Also Be Dangerous

More capability means more responsibility.

Suppose an AI agent has access to:

If something goes wrong, the consequences can be serious.

For example:

This is why AI security needs to be treated as part of application security.

I would never give an AI agent unrestricted production access simply because it can use the terminal.

The Right Way to Use AI Coding Agents

For production software, I prefer this workflow:

The agent can do a lot of implementation work.

But the developer remains responsible for approving the result.

AI Should Not Directly Control Production

This is especially important for SaaS applications.

I would avoid:

Instead:

The more sensitive the system, the more important these controls become.

Testing Becomes More Important

There is an interesting side effect of AI-generated code.

If AI can generate code faster, we can also generate bugs faster.

So testing needs to improve along with coding speed.

For example:

This creates a feedback loop.

A strong test suite becomes a very useful tool for an AI coding agent.

Your Tests Become Part of the AI Context

Suppose my application has 1,000 automated tests.

The AI makes a change.

Then:

The failures provide useful feedback.

The agent can inspect the errors and try to correct its implementation.

This means a well-tested codebase is not only good for humans.

It can also make AI-assisted development much more effective.

AI Is Also Becoming a Debugging Partner

One of my favourite uses of AI is debugging.

For example, suppose a Laravel queue job takes 30 seconds.

I can give the agent:

Then ask:

"Find the most likely performance bottleneck and propose a production-safe fix."

The agent can investigate multiple parts of the system.

It may discover:

But the final recommendation still needs developer verification.

AI and Code Review

AI can also become another reviewer.

For example, after creating a pull request, an AI agent could look for:

The goal is not to replace human code review.

It is to provide another layer.

This can become particularly useful when teams are producing code faster because of AI.

The Biggest Risk: Blind Trust

The biggest mistake developers can make is:

"AI generated it, so it must be correct."

No.

AI can produce:

Sometimes the code looks excellent and still contains a serious problem.

That is why I would always ask:

These questions remain important even when AI writes the code.

AI Coding and Junior Developers

This is another interesting question.

Will AI remove the need for junior developers?

I don't think it is that simple.

But the entry-level skill requirements are changing.

A junior developer who only knows:

will have more competition from AI.

A junior developer who understands:

will be in a much stronger position.

The important skill is not only knowing how to type code.

It is knowing whether the code is correct.

The Importance of Adaptability

Technology is changing faster than traditional development education.

Recent discussions in India are also highlighting adaptability, critical thinking and the ability to work with AI as increasingly important skills for technology professionals.

I think this applies to experienced developers as well.

We cannot say:

"I have been using PHP for ten years, so I don't need AI."

At the same time, we should not say:

"AI can code, so I don't need to understand programming."

Both approaches are wrong.

The better approach is:

What Should Developers Learn in 2026?

If I were creating a learning roadmap today, I would focus on:

1. Programming Fundamentals

Don't skip the basics.

2. Database Engineering

Understand:

3. APIs

Learn:

4. AI APIs

Understand:

5. AI Agents

Learn:

6. Testing

Learn:

7. System Design

This may become even more important.

Understand:

AI Is Making Code Cheap

This is a thought I have been thinking about a lot.

If AI can generate thousands of lines of code quickly, then code itself becomes less scarce.

The scarce things become:

This is a major change.

In the past, writing code was often the expensive part.

In the future, understanding what code should be written may be the more important part.

What About Open Source and Local AI?

Another important trend is local AI.

The open-source ecosystem is making it easier for developers to run capable AI models and agents locally. NVIDIA recently highlighted the growth of this ecosystem and the increasing ability to customize and run AI systems on local hardware.

This can be useful when developers care about:

For example:

The main limitation is still hardware.

Larger models require more memory and computing power.

But this area is developing quickly.

AI Coding Is Also Changing Software Companies

The effect is not limited to individual developers.

Companies are also reconsidering how software teams work.

If a team can produce more software with the same number of developers, companies may start focusing more on:

At the same time, software organizations need to make sure increased development speed does not create a huge maintenance problem.

More code is not automatically better.

More Code Can Actually Become a Problem

Imagine an AI agent creates:

Everything works.

But six months later:

This is why developers still need strong engineering discipline.

AI should help us create better software, not simply more software.

My Approach to AI-Assisted Development

If I am using an AI coding agent on a real application, I would follow these principles:

This gives us the productivity benefits of AI without giving up engineering discipline.

The Future Developer

I don't think developers are disappearing.

I think the definition of a developer is changing.

The future developer may look more like:

Instead of manually implementing every small detail, developers will increasingly orchestrate AI systems to implement those details.

The ability to review and control the result will become extremely important.

Final Thoughts

AI coding agents are becoming one of the biggest changes in software development.

Google's Gemini 3.7 Flash is explicitly targeting coding and agent workflows, while Meta's Muse Code is aimed at working across large and complex codebases.

For developers, I don't think the correct response is to fear these tools.

We should learn them.

But we should also understand their limitations.

AI can write code.

AI can debug code.

AI can test code.

AI can work through large tasks.

But developers still need to decide:

That is where engineering judgment becomes extremely valuable.

My biggest takeaway is simple:

Don't compete with AI by trying to type code faster. Learn how to use AI to build better software faster.

The developers who learn this mindset early will have a significant advantage as software development continues moving toward AI-assisted and agentic engineering.