Getting Started With Coding Agents - What I'd Tell a Friend

As with most things related with AI, coding agents is a hot topic and there's a lot of different opinions floating around on the Internet. Some say "coding is essentially solved". Others say it's all just "AI slop", meaning it's code of bad quality that would never be good enough for professional software development. As so often, I think the truth is somewhere in between.

I think there is no way to ignore this if you're working in tech, and I think the best way is to start exploring it yourself so that you make your own experiences and can form you own opinion, grounded in facts, not beliefs and anecdotes.

I was myself very skeptical of AI and coding agents. Which is ironic given that I've been a researcher in machine learning for more than a decade. A friend urged me to try this out, and he suggested strongly that I should directly use Claude Code, not more limited products like github copilot. Eventually he convinced me around August 2025 and for me at least it immediately clicked.

Since then, I've built a lot of things, an Instagram clone for my family, a language tutor with vocabulary list, an LLM driven mini-textadventure, I've migrated all my blogs from Jekyll, wordpress, and self written stuff, wrote my own CMS that I'm using right now, I've given my twitter archive a new home. I've written some small tools to help with development.

I can confidently say that this stuff mostly works amazingly well, and it has gotten much better over the past months. And it's fast. It takes 10-15 minutes to implement the first version of any idea. There are short-comings as well. Debugging is still frustratingly weak sometimes, the sycophancy (always agreeing with me, whether my ideas are good nor not), and the personalities are sometimes annoying (like the tendency to always offer a follow up, always wanting to build something, or they are just not fit for certain kinds of work). What I haven't seen is genuinely bad code, on the contrary. In particular for standard things like web applications I'd even say the code is very clean.

On the other hand, I'm aware that many people have much less positive experiences. There have lately been a couple of studies by credible organizations about the effects of AI, and they warn about "AI burnout" and "AI brain fry". They observed that people take on more responsibility and tasks once they are becoming faster thanks to AI, and the constant context switching and the blurring of work time and private life can be problem. I can totally see that this work can be disorienting because of the complexity and the speed, but I don't agree that the root cause of this is AI, but how we change our expectations and how we work with AI, and that's something within our control. The article also don't consider the reduction in load and complexity that comes from using a coding agent. Somehow, the picture they paint is that you get done more in less time, and the pressure is just increasing proportionally, but I don't think that's true.

So, how do you get started to figure out what it means for you? My main recommendations:

• Start small. Pick something small to implement from scratch. Don't try to use coding agents directly on you 100k codebase at work. If you don't have ideas, build a blog engine or a to do list like countless generations before you! A piece of software can be a home-cooked meal, now more than ever before.

• Take it slowly, use it interactively, read edits before you approve them. This way you can get a feel for how it works, how it would compare to what you would be doing, where it fails. As you will go to more advanced features and uses of AI, this will help you a lot. Also, reading along is much better than reviewing a 500+ line edit after the fact.

• Explore creating specs and plan mode. Talk with an AI about architecture (could also be chat bot like chatGPT, Gemini), then let it write a SPECS.md file and let the coding agent use this to implement, or use the plan mode in coding agents to create the plan internally. Having the understanding what works and what doesn't will help to know when to be specific and when it will figure it out by itself. At this stage, you'll be auto-accepting edits but maybe still read along to get a feeling what's happening.

• Learn how to guide the coding agent when debugging. Instruct them to form hypotheses and test them, instead of throwing fixes at them. Watch out for "easy out" explanations like "race condition". LLMs like them because then there's no follow-up to do.

• Only then start looking into more complex topics like skills, subagents, and fully spec-driven development. I see people trying to use these features before having a good understanding of how coding agents "think", and I think that's part of why they struggle. Just consider this like any other tool. You have to learn the basics before you can confidently do more complex things.

Here are a few things to watch out for, based on my experience, and the studies that I mentioned above:

• Don't fall for easy explanations when it doesn't work. "It's hallucinating" is the new "it's a kernel bug". We humans like those explanations that are somewhat out of our control. When building Talk with Ren I've had issues where the LLM was doing loops on function calls, and I blamed it on "small models are just bad at it" till I realized I had forgotten to put the function call result back into the context. To the LLM, it looked like function calls never worked and it just kept retrying.

• Make sure you don't exert yourself. There are many reasons to spend more time with these tools than it's good for you. They are fascinating. They can allow you to make progress faster than ever before. They are designed to be addictive and engaging. I set Claude Code up so I can ssh from my phone to it running on a raspberry pi at home. But you need to know when enough is enough.

• This is high density work, don't underestimate the cognitive load. These are not "fire and forget" tools, the technology isn't there yet, maybe never will be. You have to read along with one eye what they are doing, and there is a lot happening. I'm guilty of having two Claude's in different windows working on different things sometimes, but you have to know your limits.

My last recommendation is to spend some time understanding the technology both theoretically and practically. There are so many free or really cheap services out there to get you started sending requests to an LLM. Ollama can easily spin up a small model on your local computer, and small models are even more instructive because they are so much worse at hallucinating and consistency than bigger models. This knowledge will also help to form your own opinion about what works and what doesn't.

In any way, hope this gave you some guidance how to get deeper into this, and I'm curious to see what I'll be thinking about this post 3 months from now :)