Agentic Coding Is a Trap

Interestingly I’ve learned more about languages and systems and tools I use in the last few years working with agentic coding than I did in 35 years of artisanal programming. I am still vastly superior at making decisions about systems and techniques and approaches than the agentic tools, but they are like a really really well read intern who knows a great deal of detail about errata but have very little experience. They enthusiastically make mistakes but take feedback - at least up front - even if they often forget because they don’t totally understand and haven’t internalized it.

The claim you should know everything about everything you work on is an intensely naive one. If you’ve worked on a team of more than one there’s a lot of stuff you don’t totally grok. If you work in an old code base there’s almost every bit of it that’s unfamiliar. If you work in a massive monorepo built over decades, you’re lucky if you even understand the parts everyone considers you an expert in it.

I often get the impression folks making these claims are either very junior themselves or work basically alone or on some project for 20 years. No one who works in a team or larger org can claim they know everything in their code base. No one doing agentic programming can either. But I can at least ask the agent a question and it will be able to answer it. And after reading other people’s code for most of my adult life, I absolutely can read the LLMs. The fact a machine wrote crappy code vs a human bothers me not in the least, and at least the machine will take my feedback and act on it.

As a senior developer, 25+ years, I have been thrown recently into a meeting "hey can you join in for 5 mins". I really don't like these meetings where you're dragged in in the middle of them without any clue.

The questions came flying in fast, without any introduction, and this was about an external integration out of a dozen. They have their own lingo, different from ours, to make the situation worse.

I had a _very hard time_ making sense of the questions, as I indeed relied heavily on a model to produce these integrations (extremely boring job + external thick specs provided).

I'm still positive these would have simply not happened in a 10x the time if I did not use models, however, I'm now carefuly considering re-documenting the "ohhs" and "aahs" of these so that these kind of uncomfortable moments never happen again.

I haven't felt so clueless and embarassed in a meeting, ever. All I could say was "I'll get back to you on that one, and that one, and this one".

Cognitive debt is very real, and it hurts worse than technical debt on a personal level! Tech debt is shared across the team, cognitive debt is personal, and when you're the guy that built the thing, you should know better!

To be continued... But from now on, the work isn't done if I don't get a little 5 mins flash-card type markdown list of "what is this" and "what is that", type glossary.

> only a skilled developer who's thinking critically, and comfortable operating at the architectural level, can spot issues in the thousands of lines of generated code, before they become a problem.

An additional factor: to find issues in generated code, the developer has to care. Many developers (especially at big firms) are already profoundly checked out from their work and are just looking for a way to close their tickets and pass the buck with the minimum possible effort. Those developers - even the capable ones - aren't going to put in the effort to understand their generated code well enough to find issues that the agents missed. Especially during the current AI-driven speed mania.

I kind of think this article misses the mark a little.

There is skill loss from heavy AI use.

But I want to acknowledge the awkward elephant in the room. AI Is making people too fast. I don't mean that a faster output is bad. It's a faster output and code rather than a full understanding and experience in producing the code. It's rewarding people who try to talk about business value rather than the people that are building and making safe decisions with deep knowledge.

AI: Yes, its good and it can produce some good solutions, however it ultimately doesn't know what it's doing and at the best of cases needs strong orchestrators.

We're in a cesspit of business driven development and they're not getting the right harsh and repulational punishments for bad decisions.

>and then pulls the slot machine lever over and over

Does anyone really do this? You want verification and self-correction in a loop, not rerolling and cherrypicking. The non-determinism point is really tiresome to hear over and over.

The thing is the code quality is still ultimately up to you

Nothing stopping you from iterating with the agent till the code is the exact same quality that you yourself would write

the funny thing is once the llms got mostly good enough in november 2025 for me, it was mind boggling how much it helped me get stuff out of my head with ease.

its easier for me to code now, because its like i have a 24/7 insane intern that needs to be supervised via pair programming but also understands most topics enough to be useful/ dangerous.

ironically ive been spending much of my time iterating on ways to improve model reasoning and reliability and aside from the challenge of benchmark design, ive had some pretty good success!!

my fork of omp: https://github.com/cartazio/oh-punkin-pi has a bunch of my ideas layered on top. ultimately its just a bridge till i’ve finished the build of the proper 2nd gen harness with some other really cool stuff folded in. not sure if theres a bizop in a hosted version of what ive got planned, but the changes ive done in my forks have made enough difference that i can see the different in per model reasoning

The slot machine lever is my least favourite opinion on the subject.

Also, let’s not forget. The developer is rarely the person pitching the feature, and is normally given the constraints and the PRD…

Soooo people can keep tiptapping on the keyboard, but eventually they need to open their mind to the possibility that “the old way” is actually dead.

> When a sysadmin moved to AWS, they didn't feel like they were losing their ability to understand networking.

Wait, is this the same AWS I have been using?

AI doesn't automatically make us better human beings, but they only expose our worst parts. Most people are not born great leaders and managers (need rigorous training and experience), and empowering them with AI kind of pushes them into a spot where they suddenly need to "lead". To fight brainrot from AI overuse, we must try harder to maintain that developer's priority list.

I've been using AI tools to brainstorm approaches and sometimes generate code, but actually doing the typing myself. That way I'm less likely to forget the mechanics and programming language over time.

I think of it as driver's seat vs back seat vs passenger seat. You always take the back seat and eventually you will forget how to drive. You insist on always being in the front seat and you will miss out on the occasions where the LLM happens to know the area very well, like working with an unfamiliar library or problem domain. If it is a place that you are just passing through, it's a great to let it take the wheel and see where it will takes you. If it is a place that you need to become familiar with, it's great to have a dependable navigator beside you.

My sense is that a decade from now, the people who generally see their place as the driver seat but recognize when its not are going to be writing the code that matters.

I've come to the conclusion that if AI can do it, its not hard. None of the complicated software i work on can be reliably written by ai yet

it's a fairly new way of doing things. I predict, in the future it will be more formalized and standardized like AGILE and SCRUM and all that boring stuff.

The result of that though would be establishment of development patterns that are good practices.

The rule of thumb is: An agent can write it, but a human has to understand it before it gets pushed to prod.

I'm still not convinced about the doom and gloom over developers being replaced. I'm not a dev as part of my main job function, but where I do use LLMs, it has been to do things I couldn't have done before because I just didn't have time, and had to de-prioritize. You can ship more and better features. I think LLMs being tools and all, there is too much focus on how the tool should be used without considering desired and actualized results.

If you just want an app shipped with little hassle and that's it, just let Claude do most of the work and get it over with. If you have other requirements, well that's where the best practices and standards would come in the future (I hope), but for now we're all just reading random blog posts and see how others are faring and experimenting.

Lars we are on the same page. I use LLMs to help me scope and get a second set of eyes on the high levels of a task. Then I write the code. Often I automate boilerplate or boring objects but sometimes it's faster/better for me to just write them. Then I will ask an LLM to say write some tests. Then I will focus on the cases they missed and write those myself.

I have been described as a decel and a Luddite though so be weary of my opinions.

Re vendor lock in point: this is a harness issue really. Sure, CC is restricted to Anthropic models, but it's not the only harness out there. So if one vendor has an outage or botches the quality of their models due to compute shortage, you can switch to another vendor. LLMs are the easiest to switch. Of course, if hardware costs go up, so will all AI vendors. The only way out for the employer would be to directly buy the hardware (or do a fixed price deal with a cloud provider).

Re the understanding code point: you can still use LLMs to understand code. If you write the spec without knowing anything about the code, of course the architecture might suck. Maybe there is already a subsystem that you can modify and extend instead of adding a completely new one for the new feature you are adding, etc.

I use LLMs for my daily workflows and they do understand code perfectly and much more quickly than if I read it.

I can’t say that I’ve felt my skills atrophy, but I’ve also never found backend web development to be that difficult. 90% of my job for my entire career could be described as digital plumber.

This author assumes that workforce development is a first-order priority for businesses, or at least for the health of the industry.

Why make this assumption so confidently?

The arrival of the electronic computer did not turn human computers into programmers, it simply eliminated them en masse.

This is how I feel about things. Its like someone is demanding that I become a manager, when I was perfectly happy being a IC. And now I have to figure out how to be a manager of AI agents while at the same time not lose my ability to judge their work, or plan effectively, even though I'm not supposed to be doing things "by hand" anymore. But doing things "by hand" is how I reasoned through problems and figured out the plan to begin with.

Would like to see a study of brain scans during flow, manual programming, compared to code review. If the conclusion is different parts of the brain are activated, then orchestration is a separate activity entirely. Reading code is not the same as writing code.

However, the code review study needs to compare between surface scanning and reviewing long enough to get over a theoretical slough of perspective: when you assume the coding chair and are in their frame, whether the brain shifts into a different cognitive mode.

Otherwise, just stamping "Looks good to me" is likely to lead to the same atrophy. There's no critical thought, even a self-summary of the change or active questioning.

Thoughtful, deliberate code review just plain takes longer. AI can help here a lot, although it still takes over the "get into review mode" process.

There’s too much in this article to comment on it all, but if we zoom into the first claim:

> An increase in the complexity of the surrounding systems to mitigate the increased ambiguity of AI's non-determinism.

My question is why isn’t there an effort from the author to mitigate the insane things that LLMs do? For example, I set up a hexagonal design pattern for our backend. Claude Code printed out directionally ok but actually nonsensical code when I asked it to riff off the canonical example.

Then, I built linters specific to the conventions I want. For example, all hexagonal features share the same directory structure, and the port.py file has a Protocol class suffixed with “Port”.

That was better but there was a bunch of wheel spinning so then I built a scaffolder as part of the linter to print out templated code depending on what I want to do.

Then I was worried it was hallucinating the data, so I wrote a fixture generator that reads from our db and creates accurate fixtures for our adapters.

Since good code has never been “explained for itself 100%, without comments”, I employ BDD so the LLM can print out in a human readable way what the expected logical flow is. And for example, any disable of a custom rule I wrote requires and explanation of why as a comment.

Meanwhile, I’m collecting feedback from the agents along the way where they get tripped up, and what can improve in the architecture so we can promote more trust to the output. Like, I only have a fixture printer because it called out that real data (redacted yes) would be a better truth than any mocks I made.

Finally, code review is now less focused on the boilerplate and much more control flow in the use_case.

The stakes to have shitty code in these in-house tools is almost zero since new rules and rule version bumps are enforced w a ratchet pattern. Let the world fail on first pass.

Anyway, it seems to me like with investment you can slap rails on your code and stay sharp along the way. I have a strong vision for what works, am able to prove it deterministically with my homespun linters, and am being challenged by the LLMs daily with new ideas to bolt on.

So I don’t know, seems like the issue comes down to choosing to mistrust instead of slap on rails.

Edit: I wanted to ask if anyone is taking this approach or something similar, or have thought about things like writing linters for popular packages that would encourage a canonical implementation (I have seen some crazy crazy modeling with ORMs just from folks not reading the docs). HMU would love to chat youngii.jc@gmail

I try to make understanding the bottleneck and it seems to work out for me while still delivering solid productivity gains.

"Don't vibe code" but here's a deadline that's impossible without it. classic

Nope. 1) Skills don't go away, you just get better at the things you do regularly, but your still have your old skills, 2) You only have vendor lock-in if you use lock-in devices (stop using Claude Code), 3) It's not an increase in complexity, it's a replacement, in order to gain efficiency (see: the cotton gin), 4) The increased cost is negligible considering average salary and resulting productivity

This is exactly the same problem of "mechanical engineers' job is to design parts, not machine them, so we'll take training on machines out of the mech eng curriculum." Result: fresh mech eng grads do not know how to properly design parts because they have no idea how they are machined.

Only way to cope with this I found is to grind leetcode or advent of code. It's kinda funny how fast this all changed. Less funny part is the fact that I'm now kinda feared for my job in some time.

Im seeing the word "agentic" a lot here. Is there a difference between "Agentic Coding" and "I put prompt into gpt or claude and pasted code into my file" ?

I think ignoring all else, generating code is not a new layer of abstraction. It's the same abstraction, we just have codegen machines now. The same skills are important regardless if the person is typing in the code or if a machine is producing it.

Agents are a first-generation technology. They propose and act at the same time. I recommend you read https://safebots.ai/agents.html

How can we solve this at a more fundamental level?

I think many people already recognize the problem:

-“Our ability to write code is being damaged.” -“If our ability to write code declines, our ability to recognize good code also declines.”

But the problem is that the market no longer works without LLMs.

Freelance rates and deadlines are now calibrated around LLM-assisted output. Even clients who write “do not vibe code” often set deadlines that are impossible to meet unless you use something like vibe coding. The client’s expectations themselves are becoming abnormal.

That is the irony of the market.

I honestly do not know what to do.

Recent Hacker News discussions are mostly a negative echo chamber about AI use. In other places, it is often the opposite: only positive echo. But almost nobody discusses the actual solution.

The main topics I keep seeing are roughly these:

1. Is the large repository PR system failing a fundamental stress test? Or should AI-generated(GEN AI) code simply not be merged? If PR review is moving from handmade production to mass production, how should the PR system change? Or should it remain the same?

2. As vendor lock-in continues, can we move toward local LLMs to escape it? Are cost and harness design manageable? What level of local model is required to reach a similar coding speed?

3. If we are forced to use agentic coding, how do we avoid damaging our own ability to code? There is a passage from Christopher Alexander that I keep thinking about:

“A whole academic field has grown up around the idea of ‘design methods’—and I have been hailed as one of the leading exponents of these so-called design methods. I am very sorry that this has happened, and want to state, publicly, that I reject the whole idea of design methods as a subject of study, since I think it is absurd to separate the study of designing from the practice of design. In fact, people who study design methods without also practicing design are almost always frustrated designers who have no sap in them, who have lost, or never had, the urge to shape things.” — Christopher Alexander, 1971

This quote feels relevant to programming now. If we separate the study and supervision fo programming from the actual practice of making, something important may be lost.

In architecture, there is this idea that without practice, the architect loses meaning. But now the market is forcing the separation.

People with enough symbolic capital and high status have the freedom not to use AI. But people lower in the market are under pressure to use it.

So I think the discussion now needs to move beyond whether AI coding is good or bad.

The real question is How do we keep using AI because the market demands it, while still preserving the human practice that makes programming meaningful and keeps our judgment alive?

I think these are the important question. How do you maintain market value without using AI?

Or, if you do use AI, how do you avoid being treated as low-quality?

If you do not use AI, how can you remain more competitive than people who do use it?

If you do use AI, what advanatge do you have over people who do not use it, and how should you position yourself?

I know that agentic coding can cause skill degradation. I can feel it happening to me already. But for someone like me, who does not have strong status, credentials, or symbolic capital, social and market pressure makes AI almost unavoidable.

What frustrates me is that I do not see practical answers anywhere.

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