Anurag is an experienced journalist and author who’s been covering tech for the past 5 years, with a focus on Windows, Android, and Apple. He’s written for sites like Android Police, Neowin, Dexerto, and MakeTechEasier. Anurag’s always pumped about tech and loves getting his hands on the latest gadgets. When he's not procrastinating, you’ll probably find him catching the newest movies in theaters or scrolling through Twitter from his bed.
It's incredibly easy to ask AI to write code for you, and when it does a good job, you start to wonder why you need to learn to write it yourself. I used to think the same way. I don't come from a traditional development background, and I've only recently started learning how to program. But what has helped me become better at coding and building things with AI agents is actually taking a more hands-on approach instead of asking them to do everything for me.
I know there's a valid argument for 2026: why bother learning to code when AI can already do it for you? I've asked myself that question, too. But I've seen instances where, if I use AI to build something from scratch, I have no clue how the code works. When something breaks, I keep going in circles because the AI cannot figure out what's wrong, and since I wasn't involved in the process at all, I have no clue either.
Stop asking AI to implement everything
Understand what your agent is doing
When I first started using coding agents like Claude Code and Codex, my approach was pretty straightforward. I'd describe what I wanted to build, give the agent access to my project, and let it handle everything from writing the code to testing the implementation. If something didn't work, I'd simply ask it to fix the problem. This was incredibly convenient, and I built things I wouldn't have been able to create on my own.
However, I eventually realized that I wasn't learning much from the process. I could explain what my application did, but if you asked how a particular feature worked under the hood, I struggled to answer. So I started changing how I worked with these agents.
Now, instead of asking an agent to implement an entire feature, I first ask it to explain what needs to be done, which files need to be modified, and how the different components interact. I break the implementation into smaller tasks and try writing some of the code myself. When I get stuck, I ask the agent to explain the problem or suggest an approach rather than immediately generating the solution.
I still use agents to write boilerplate code, handle repetitive tasks, and work on things that are beyond my current understanding. There's little value in manually writing every line just for the sake of it. But by taking responsibility for parts of the implementation, I've started understanding how the code actually works. More importantly, when something breaks, I have a much better idea of where to look instead of blindly asking the agent to fix everything.
You don't need to learn programming the traditional way
It's not the same anyway
I don't think you need to spend months learning a programming language from scratch before you can start building something useful in 2026. There are plenty of programming fundamentals worth understanding, but I also don't see the point in memorizing syntax or writing repetitive code when an AI agent can handle those things in seconds. The way I see it, the goal is to understand what you're building and how it works, even if you're not the one writing every line of code.
For example, understanding how functions work, how an application communicates with an API, or how data moves between different components is far more useful to me than remembering the exact syntax for every operation. If I understand these concepts, I can explain what I want to an agent, evaluate its approach, and recognize when something isn't working as expected. For everything else, I can refer to documentation or ask AI for help.
I've also found that learning through actual projects works better for me than trying to learn everything in advance. When I encounter something I don't understand, I ask the agent to explain it, look at how it's implemented, and try making changes myself. This way, I'm learning concepts that have an immediate application instead of working through exercises that I might never use.
Of course, if you're planning to become a professional software engineer, you'll need a much stronger foundation in programming and computer science. But for someone like me, who primarily wants to build useful tools and applications, I think it's perfectly reasonable to let AI handle the repetitive work and spend more time understanding the decisions that actually matter.
More code doesn't mean better software
We're generating more code than ever
One thing I've noticed with coding agents is how easy it is to end up with a lot more code than you actually need. You ask an agent to implement a feature, and it might create several new files, add dependencies, or write functions that do something your application already handles elsewhere. The feature works, so you accept the changes and move on. But every unnecessary addition makes the application more complicated to maintain, especially when you're still learning how everything works.
And this is becoming a larger problem with AI-generated code. According to GitClear's 2026 research, which analyzed 623 million code changes, code block duplication increased by 81% compared to 2023. The research also found a significant decline in refactoring and code reuse. We're generating more code than ever, but that doesn't necessarily mean we're building better software.
This is another area where I've realized that understanding programming makes a difference. When an agent proposes a solution, I want to know whether it needs to introduce new functions, whether it can reuse existing code, and whether there's a simpler way to do the same thing.
Programming isn’t the same in 2026
This is probably the most obvious thing I've said in this entire article, but programming has changed significantly in 2026, and I don't want to be in denial about it. You shouldn't be either. AI and coding agents are now an integral part of programming, and there's no point pretending otherwise. Whether you're working on the front end, back end, or anything in between, expectations from developers and software engineers have changed. You're expected to deliver a lot more in the same eight hours.
I still think learning to code is important. The more you understand the code you're working with, the better you become at using these agents.