The Four Horsemen of Agentic Coding

Agentic coding is undeniably very useful. It’s also very bad. Or rather, it’s having some horrible effects on us, our craft, and our relationships with each other. I can feel it in my bones, and a lot of others can too. But whenever I try to explain what exactly is wrong, I find myself waving my hands wildly and jumping from point to point. What do you mean, what’s wrong? So many things are wrong!

Fine, here’s a list. 4 problems with no solution in sight–or, as I call them, the four horsemen of agentic coding.

LLM-generated code has a strong smell that makes codebases repulsive to humans.

LLMs aren’t humans, and the way they write code is... different. Not strictly worse, but different for sure. We quickly came up with a name for it—slop—and it undoubtedly carries a negative connotation.

For plain prose, humanity seems to be converging on the opinion that AI prose is bland at best and an insult at worst. For code, due to its instrumental nature, the debate is far from over. A lot of people latch on to the idea that very soon we won’t have to read the code at all. “Remember, it’s the worst the models will ever be,” they say.

And yet, the latest generation of models is surprisingly underwhelming. They’re certainly smart, but they seem to be moving towards “unemployable smart” rather than “inspiring smart.” Claude now famously communicates entirely through word salad, and Astra writes in a bizarre competitive code-golfy style, incomprehensible to normal humans. Sloppiness turned out to be a surprisingly persistent property of LLM output and at this point looks like a signature move rather than a growing pain.

Which is very bad news for people who still want to poke around in the codebase and maintain some familiarity with it, especially in a team setting. Once agents are allowed, they very quickly take over. What used to be a shared space for humans where each team member contributed became an AI wasteland where people don’t want to spend time.

But what if we don’t have to spend time in these slop caves? Why spend your time there when you can be a design mastermind and reign over a swarm of loyal agents from the comfort of the chat interface?

Engineers become alienated from code and, as a result, care less.

Software engineering used to be a fairly... kinesthetic field of work. Maybe not to the same degree as woodworking, but we loved our tools too, both real and virtual. Text editors had a cult-like following. Popular color themes and programming fonts had their own fanbases. Split keyboards were a hot thing once, and there was no shortage of keyboard sound testing videos.

We produced code directly, by hand. It’s only natural that this resulted in a strong sense of ownership of the fruits of our labor. Sure, this feeling varied depending on the context, but even if you just assembled something from a set of building blocks, you would feel something. IKEA certainly knows that!

Agentic coding has dramatically increased the distance between engineers and the product of their work. This gap is so large that none of our kinesthetic tools can cross it. We don’t experience code directly anymore: we send an agent with a vague set of instructions and then skim through its report. Hell, even typing is optional—you can just yap at your phone while driving.

So naturally, we become more detached. We care less. We lose touch with our craft. And it shows. It shows in poorly thought-out features that receive no pushback. It shows in band-aid bugfixes that don’t address the root cause. It shows in how unfulfilled we feel at the end of the day. For software to spark joy, it has to be imbued with its creator’s love.

AI erodes existing skills, impedes learning, and doesn’t offer any meaningful skill progression.

I think at this point it shouldn’t be controversial to say that deskilling is real. There is no shortage of people reporting their wits slipping away after prolonged use of AI. Which makes sense: a lot of things in life are use-it-or-lose-it, and professional skills are no exception. Yes, you’ll always know how to ride a bike, and I myself will die before I forget how to exit Vim, but anything beyond that is fair game.

I’ve made the point before that we’re currently experiencing a historical anomaly. We have all those seasoned engineers who spent years doing things the hard way and can now hold LLMs exactly the right way to be insanely productive. But unless we figure out how learning works in the age of AI, this supply won’t last long.

One common piece of advice is that if you’re at the beginning of your career, you simply shouldn’t use AI. And I completely agree, but it’s important to understand that this isn’t a viable strategy for humanity as a whole. Everyone will not just.

We can’t be blind to the fact that the introduction of an “easy button” has led to a tectonic shift in incentives to learn. And if I know one thing about humans, it’s that they follow incentives.

Another common response to the deskilling argument is that LLM usage comes with its own learning curve. You just need to upskill and learn the intricacies of downloading markdown files! I’m not buying it. The learning curve is basically non-existent.

A printing press or a chainsaw at least requires some skill to operate, but the fancy new magic box? You don’t have to know how to read or write. A caveman could build a B2B SaaS and be on time for his weekly podcast appearance. And if you think your custom agent orchestration workflow is an important feat of engineering, just you wait.

Agents weaken the social fabric of engineering teams by reducing interpersonal communication, increasing self-reliance, and hindering skill expression.

Slack channels used to be crowded with people. Anything that your rubber duck couldn’t answer would go into a team chat. Now the chat is a pale shadow of its former self. Everyone is too busy talking to their agents. Each of us has received a magic familiar that follows us around like we’re some kind of Disney protagonists. It’s very smart, available 24/7, and will never report you to HR. The rubber duck can finally talk back!

In a certain sense, being more autonomous is virtuous. Why distract colleagues with your silly nonsense? The problem is, helping other people with silly nonsense is our thing. It’s how we build and maintain relationships. You need to walk the path; otherwise, it will be overtaken by grass.

I’m convinced that AI has dramatically reduced interpersonal communication. The average team chat right now consists of agent threads and an occasional LLM-generated manuscript followed by a humble “what do you guys think?”

Agentic coding also changes the way we see other people. Teams used to be sort of like parties of adventurers: a Git wizard, a Rust witch, a bard who is really into mechanical keyboards. There was plenty of room for skill expression, and the diversity of expertise was celebrated.

Now, an average team consists of a Claude operator, a Codex whisperer, and a guy with AI psychosis who insists on committing .md files. “This is John—you’ll want to see how he prompts,” said nobody ever.

And that’s true: nobody wants to see how John prompts. There is something repulsive and fundamentally unsexy about talking to an agent. It feels fine when you are doing it, but others... ugh, get a chatroom. Their prompts are cringe; their agents are weird. Like looking at someone else’s TikTok feed.

So here we are, still peers in the org chart, but growing more and more distant over time. I used to praise your Vim skills and Advent of Code solutions. These days you prompt Claude all day, and I do too. Are we even a team anymore?

So that’s my list. And I’ll be honest with you, I have no idea what to do with any of this. I can only hope that eventually we’ll move forward in this cursed discussion. However beneficial AI is, we pay dearly for it with our curiosity, craftsmanship, and social connection.