AI-First Architecture: Why Single Responsibility Matters More Than Ever

AI is changing how we write code faster than any shift we've ever seen. In just a couple of years, it went from auto-complete to code developers. But the truth is while AI is accelerating everything, the principles that hold software together haven't changed. In fact, they matter more than ever.

ChatGPT began as a chat bot. Then it started writing function, reading tickets, reviewing the PR. It's learning the language of engineering, not just the syntax, but the process. In a few years, it won't just assist us. It will collaborate with us.

So what's our role? We have two choices. We can push back, pretend this change isn't happening, or we can embrace it. But do it with the structure, with guardrail, with intent, because without them, speed turns into chaos. With them, we can build a system that stays stable even when AI is involved.

We are in a transition phase. AI isn't fully a teammate yet, but every update gets closer. It reads our structure. It learns our pattern, and it adapts to how we build soon. It won't just follow our command. It will help us to decide how to build and how we're building softwares. The line between human and AI is fading. Speed ain't new. Every major leap in history began with chaos.

When we move from horses to cars, it looks uncontrollable, too fast, too risky. But once we added structure, roads, lanes, rules, whatever, that speed becomes progress. We have lived with that for a century now, and speed didn't break the world. It just reshaped it. Speed ain't the enemy. The same is happening with AI. It's not creating chaos.

It's not creating chaos. It's amplifying it. And if you gave it a structure, it's amplified progress continuously instead of that chaos. That's why architecture matters more than ever. It's about what's keeping your systems stable, even with AI taking the wheel to evolve them.

I'm John Adib. I've spent the past decade building systems of all sizes, from start-up small apps to large scale distributed platforms, and I've seen both chaos and control up close. My focus has always been simple. Make it clear, consistent, and build to last. And lately, AI has become part of that story.

When I first started using AI in production work, it was like adding a new teammate. Fast, creative, but somehow unpredictable. And I realized something. The same architecture principle, whatever we have, that's helping humans to collaborate more effectively with each other, is also helping AI to fit in. That's what this talk is about. How to stay in control, while everything around you is accelerating.

We've been through many shifts, from cloud, from desktop to cloud, from monolith to microservices. But this one is different. This is new. This is entirely changing the way of working that we have. AI isn't a new framework or library. It's a new participant about how we build. So what does that mean for our architecture?

Traditionally, our systems were designed for humans only. AI, if it exists, even, was the external service, just through some kind of API. The architecture doesn't really care about AI as a part of the team and the process. But that's changing. It's changing fast. AI can now read our tickets, generating the PRs, refactoring our code, or even reviewing the code and ship the code to production. It's no longer a tool. It's part of the entire process.

It can participate in each of the small parts of the process. That means our architecture has to adapt. To me, AI-first architecture means building a system that doesn't collapse when AI joins the loop. Systems with clear boundaries, predictable layers and strong guardrails, so AI can move fast without breaking your structure. And your code and your system.

AI doesn't invent a structure for you. It just amplifies whatever you have there. If your foundation is clear, it's amplifying the clarity for that. But if it's messy, it's just amplifying the chaos and it's going to fail very fast. That's why architecture, especially single responsibility, matters more than ever.

Now, the principles that keep everything stable, single responsibility. Let's speak about that. If I had to just pick one principle to keep everything stable in the era of AI, it would be this one, the single responsibility principle. It says simply one file, one purpose, one reason to change. It sounds very simple. It's very basic. Every engineer knows that. But it's the rule that separates chaos from clarity, especially in the era of AI.

So single responsibility isn't just about the syntax. It's about intent. Every file, every function and component that you have should have just one job and do it really well. When boundaries are clear, AI understands them, too. It knows where the things belong and it's going to connect the dots together. And then it's going to create whatever you ask for fixing the bug or adding a new feature. And it's not going to break. AI learns by pattern recognition.

If your architecture has a clear separation, it's become clear for AI to read that and understand that. But if everything is mixed, logic, AI, backend, database, whatever you have, AI learns all of those noises. It's going to amplify those. And it's going to create a mess more and more. Let's imagine this for a second. You open your cursor chat, Cloud Code, Gemini, Chattopaddy, whatever you have, any AI that you have, and you ask it for adding a new small feature to the code base. It's going to start quickly, jump right in, adding more and more lines to the existing code base. It doesn't think about what you have there, your architecture. It's just going to complete your prompt ASAP.

AI doesn't care about your architecture. AI cares about your prompts. Unless you design a system that teaches the process to it. Single responsibility and separation of concern. These two are siblings. One defines the rule, another one shows how to apply them. Single responsibility says one file, one reason to change. And on the other side, separation of concern says make sure each part of your system handles its own responsibility and nothing else. When these two boundaries are clear, humans understand the system. Also, AI can follow them to follow the structure that you have more accurately in your code base. Now, imagine the same feature again, but this time, your project has a clear structure. The AI looks at the pattern that you have and starts following it.

The AI looks at the pattern that you have and starts following it. It doesn't add a code just anywhere it can do. It's trying to fit the best place for that logic. The speed is still there, but now it's become stable, predictable and also easy to maintain, which is really important for us.

We are not going to just build the things and just publish them. We need to maintain our software and that's really matter. Because when architecture guides AI, structure turns speed into stability. The difference between these two is the time that the architecture guided the AI. So principles, as we spoke, give us a direction and guardrails keep us on track.

Structures give us direction, but as we said, the guardrail gives us a direction to be safe. AI is moving fast, but without any limit, it's going to very quickly become a risk. Let's look at the simple rules that protect stability when AI joins the build. Lint. We can't rely on everyone or every AI to remember our boundaries. Lint rules and a static check is going to make the structure non-negotiable. They turn clarity into code.

The best system doesn't just depend on the memory of the people, it depends on the enforcement. When we're discussing Lint, we need to define rules that apply regardless of whether AI or humans are writing code to prevent issues and chaos. Various rules exist based on the language used, and selecting the appropriate tools is crucial for safeguarding your codebase and saving time in maintenance. Tests play a significant role in teaching AI correct coding practices and verifying the ongoing functionality of the code structure. They serve as a contract ensuring the code's integrity remains intact.

Tests are essential in the AI era to guarantee reliability when code is modified by others or AI in the future. Maintaining strict and comprehensive tests is critical for future code changes. Understanding the significance of tests is vital, not just as an optional practice but as a fundamental aspect of code stability. AI behavior control regarding code changes is crucial, emphasizing the importance of consistent testing practices and knowing when and why to modify tests.

Consistency in coding practices, such as naming conventions, folder structures, and functions, is key to AI comprehension and predictability. By following consistent patterns and maintaining clarity in coding practices, AI can easily interpret and navigate code. Consistency signals to AI how to operate effectively, highlighting the importance of clear and structured coding practices for successful AI integration and understanding.

But what's matter in the era of AI is really depending on the test because you're writing something and AI is going to change that in the future. So if your test is really restricted and checking the entire situation that you have, it's going to be really reliable in the future when somebody else or AI is changing that in the future. So we need to understand the importance of the test and we shouldn't just consider that as nice to have. It's going to be something really critical for us.

And the part that we're going to control our AI about changing the code and funny part, I saw lots of times sometimes, actually, one of my colleagues recently told me about the test failed. I asked AI to fix them. AI removed them all and nothing to remain at the end. That's matter is you need to understand the test is something which is entirely different level and you need to understand when and why you need to change them.

Normally, when you write a test to check a special output of some function or some entity that you have, you don't need to. You shouldn't update that in the future. And remember, the single responsibility, if you need another act, another outcome of that function, you should create a new function. Don't update that one, the old one. AI learns from our patterns. It's about consistency, consistent naming, foldering, function, everything that we have. It's all signals to AI to follow.

If humans can guess where is the code for doing something, AI also simply can do that. But the consistency is about clarity. It's helping us. It's helping the new way of coding that we have.

And the next important part is about documentation. It isn't about the comment in the code. Documentation is about giving a context, a story of how your system fits together. When architecture is clear, you shouldn't need a comment to explain it.

I believe we don't need a comment. If using descriptive names and we understand, give a context with documentation, we don't need to have in-line comments to define what's there. What matters in documentation is boundaries. It's about all intention of what's about that part of the code. That's going to help both humans and also definitely AI to understand how to build extent or to fix without guessing about them.

That's going to help both humans and also definitely AI to understand how to build extent or to fix without guessing about them. Good documentation doesn't describe the code. It describes the system. And in the era of AI, it matters. And the reason is that some of the AI are trying to first analyze your code and then generating documentation for themselves to refer for the next few things that they want to implement.

So we see some of the AI especially trying to create documentation to save tokens for next request that you have because they document whatever they needed. And next time, they're just reading that part instead of reading the entire code base to understand them. Some people think rules may slow down the team, but good rules do the opposite exactly. They keep momentum safe. GuardDays let everyone move fast in the same direction. It's not slowing down us at all.

And you need to understand that all of the GuardDays, all of the things that we spoke about and we can add to our code base, AI is going to learn about how to use them. And each time it's going to better and better. It's understanding your pattern and each time trying to act better and better. So that's you need to understand. Investing time now to improve these GuardDays for your team and AI is saving your time in the future. So I'm just suggesting do that ASAP before it becomes late.