I'm a Software Engineer, and I'm Choosing to Build With AI. Here's Why.

AI is changing software development. I don't think developers should fight that change. I think we should learn to use it properly.

I've been a software engineer for years. I've spent a good part of my career learning technologies, solving problems, debugging applications, and figuring out how to turn ideas into working software.

And recently, I've been spending more and more time working with AI.

Not just asking it questions or generating small code snippets. I mean using AI coding agents to work on actual software projects: exploring codebases, designing features, implementing changes, investigating bugs, and reviewing the results.

It's changed the way I approach development.

And I think we're only at the beginning.

We need to stop treating AI as something developers must either love or hate

There's a lot of debate around AI in software development.

Some people see it as the future of programming. Others worry that developers will become less skilled, that AI-generated code is unreliable, or that we're slowly giving away the thinking that makes us engineers.

I understand those concerns.

But I don't think ignoring AI is the answer.

Software development is changing. Whether we like every part of that change or not, AI coding tools are becoming increasingly capable. They can help developers move faster, explore unfamiliar technologies, and tackle work that might previously have required significantly more time.

That doesn't mean we should blindly trust everything they produce.

It means we need to become better at working with them.

The goal shouldn't be to let AI replace engineering judgment. It should be to use AI while keeping engineering judgment firmly in the process.

Writing code is only part of building software

One thing becomes obvious when you use AI to build something beyond a small demonstration: generating code is not the same as delivering software.

An AI agent can implement a feature that looks correct but breaks something else.

It can introduce a security weakness, misunderstand a business requirement, overlook an edge case, or make a change that works in isolation but fails when combined with the rest of the application.

And sometimes, the result looks impressive enough that you don't immediately notice the problem.

This is where the developer's role matters.

You still need to understand what you're building. You need to inspect the existing system, define requirements, make architectural decisions, review changes, test behavior, and think about what happens when things go wrong.

AI can help with these activities. It can even perform a significant amount of the implementation work.

But someone needs to remain responsible for the result.

That's the approach I want to explore.

I'm working on something called Building Real Software with AI

I've started developing a project called Building Real Software with AI — A Practical Engineering Field Guide.

The idea is straightforward:

I want to help developers move beyond generating isolated code snippets and learn how to use AI coding agents to build complete applications responsibly.

Not another collection of clever prompts.

Not a promise that you can build a production-ready business application in one weekend.

And definitely not a guide that assumes AI-generated code is automatically correct.

I want to focus on the actual engineering process.

How do you approach an unfamiliar codebase? How do you turn an idea into clear requirements? How do you give an AI agent enough direction without surrendering control? How do you review its implementation? How do you test the result? And how do you think about security, payments, data integrity, deployment, and maintenance?

These are the questions I want the project to address.

The guide uses a continuous example: a fictional multi-business digital-products platform. Instead of jumping between unrelated toy examples, the reader follows a system through different stages of development.

One principle guides the entire approach:

Inspect → Plan → Approve → Implement → Verify → Document.

AI should help you build. It shouldn't make you stop thinking.

Why start with a PDF?

I'm deliberately starting with a PDF.

I could spend months building a website, a learning platform, an interactive course, or a collection of tools around the idea.

But I'd rather start with the actual learning material.

A PDF is a practical first format. It gives me a way to organize the ideas, explain the workflow, include examples, and put something tangible in front of readers.

I'm treating this as the first version of a larger project, not pretending that the entire product experience is already finished.

There are other things I'm considering for later:

• A companion GitHub repository with a reference implementation.
• Practical exercises and templates developers can use in their own projects.
• Checklists for reviewing AI-generated code and implementation decisions.
• Additional resources and updates as the guide evolves.

I haven't committed to all of these. I want to see what readers actually find useful before deciding how far to take the product.

Who am I building this for?

Primarily, developers who already understand at least some programming but want to become more effective with AI coding agents.

Perhaps you're self-taught.

Perhaps you're a junior developer trying to build more substantial projects.

Perhaps you're a freelancer who wants to deliver better software for clients.

Or perhaps you're an experienced developer who has started using AI tools but isn't entirely comfortable trusting the results.

I don't think you need to become an AI expert to benefit from these tools.

But I do think you need to understand software engineering well enough to recognize when an AI agent is making a bad decision.

And if you don't know something, you should learn how to investigate it rather than simply accepting the generated answer.

I'm not trying to predict the future of software development

I don't know exactly what software engineering will look like five years from now.

I don't know which AI tools will dominate, which workflows will become standard, or how much of today's development process will change.

But I do know that developers who learn to work effectively with these tools will have opportunities to do more.

I also believe that the fundamentals will remain important: understanding requirements, designing systems, reasoning about data, testing behavior, protecting users, and taking responsibility for what we ship.

AI changes how we work. It doesn't make those responsibilities disappear.

That's why I'm building this project.

I want to explore what responsible AI-assisted software development actually looks like, document the process, and make the lessons accessible to other developers.

Building Real Software with AI is still evolving. The PDF is the first step.

If this is something you're interested in, I'd love to hear from you.

What has your experience with AI coding agents been like? Have you built something substantial with them? What do you wish someone had explained before you started?

I'm particularly interested in the gap between getting AI to generate code and getting AI to help deliver software you can confidently maintain.

That's the gap I want to work on.

I’m Ayman Atif, a freelance software developer. I build business software for startups, small businesses and agencies, and a good share of that work starts with an old spreadsheet or database that nobody wants to touch.