There's a reason why we talk about software development lifecycle, design, architecture, testing ... It's because it's been the most reliable way to build and ship software. We shouldn't expect discard this and expect agents to perform well outside of this.
I'm treating LLM agents as junior devs who happen to have vast knowledge of software engineering. As their team leader i make them go through planning, implementation, bug sweeping cycles using strict workflows. And it works quite well, i've been working on several large projects (1M+ LOC java,typescript,c/c++) and by any measure the projects are healthy. Sure the code isn't that beautiful, sure i'd have written things differently but it's pretty good nonetheless.
Shameless plug here: i've been also working on https://kodfactory.com, the code factory i've built to work on these large projects with workflows, reviews, etc ... I'm cleaning things up to open source it later.
because that's the end goal? and for simple small stuff they're already there?
They do often enough that it's not a surprising event, depending on prompt quality, context available, ability for the result to be objectively judged and iterate on by the agent, etc. For frontiers on very high settings at least.
because that's how agents are marketed.
heaps of people on this site expect them to be omnipotent then claim it’s fake when it doesn’t read minds
- https://openai.com/index/introducing-the-codex-app/
- https://www.anthropic.com/news/claude-3-7-sonnet
anthropic specifically brags about how good claude code is every annoucement of a new model. I will surrender that none of them claim its "to perfection", but IMO its implied because no one would claim that their model one-shots any issue to dog shit quality.
no where does this document suggest that codex can "one shot everything to perfection with just a prompt". It describes using a prompt plus agent skills (which are essentially many other prompts) to develop a playable game.. nothing about it being perfect or anything more than being in a playable state.
Besides, other people's claims about something doesn't give you license to abandon all critical thinking. Though it's evident they don't claim what you say they are.
To be clear that's not what I'm thinking, even Fable 5 produces some hilariously bad results under some conditions and sonnet 5 produced great results under others.
But you didn't provide the evidence for that. You shared some links and then admitted they didn't claim it. What?
And I don't see the problem with agents building test scaffolding as they go. It might be too defensive at times, like testing a shell script you don't run often, but big deal. It's kinda cool imo, and it's trivial to make it stop.
In effect, I’ve always wanted a pair programmer agent, not a zero to one programming agent. Unfortunately models these days are mostly of the latter kind and it has caused a major disruption in the way I work. I’d much rather appreciate a small model making fast and specific edits that I ask if it, rather than ingesting 20 files to make changes, and then starting to write tests, etc.
I think seasoned developers, over time, learn how to work a code base and design components with well defined interfaces, where the implementation is isolated in small well contained classes. SRP etc. more junior programmers can work on those smaller components/services in isolation.
For me this also seems to be a productive way to work along side an agent. Break up functionally into well defined chunks, and let the agent work on each small problem. Take more of a lead in the architecture I suppose.
I definitely prefer (c). But I get why (b) can feel necessary. If your competition is using (b) there can be pressure to do the same just to keep up.
The software has a plugin API. I asked Muse Glimmer to recommend a plugin — it found one but I tested it and it didn't work for unclear reasons (among other things the software installed version is old, the plugin older). I then asked it to outline how to implement a simple word filter, it gave me an overview of some hooks that looked right from dim-and-distant-past recollection of reading the docs when I installed it. I asked it some questions, it did the research.
I then set it off generating the skeleton of a filter plugin, went to the shops to buy food, came back and worked through filling it in and finishing it off. There was a bug. It found the solution.
It's only about 100 lines of code but it is a random old webapp and it had to look stuff up to finish it, and I think it did rather well. All on my Mac.
I am deeply cynical of the one-shot code, "nobody codes anymore" hype culture idea and that distaste put me off AI and agentic coding for ages. Like you, I want an assistant but as a freelancer I have to stay in control. I have no interest in the "just specify loops" BS and it will be bad for my business anyway.
I worry about code that I don't have a good working overivew of, and I worry that I might forget what I have done (I have pretty bad issues with focus and memory). But in this particular case, I don't really care if I forget, because there's documented code and I have no intention of specialising in this app. So it was a nice little test case.
I also don't really want to sit around waiting for Qwen 3.8 27B on this machine. Muse Glimmer is fine, actually. Gets to the solution as quickly as Qwen 3.6 35B-A3B.
This gives me a little hope that local AI will give me the sort of responsive developer sidekick I actually want.
-- PHB
So I'd rather micromanage the process step by step. It takes more of my time, but the result is much, much closer to what I actually wanted.
They handle edge cases, catch bugs, and write tests that I'd never write.
Even if, however, this leads to the average piece of software improving, this one-shot complexity has the same issues as any large project. The more code, the longer it takes to steer the ship.
This "rising tide lifts all boats" mentality will make exceptional software even rarer than it is today.
Excited for the Roller Coaster Tycoons of tomorrow[0].
[0]https://en.wikipedia.org/wiki/RollerCoaster_Tycoon_(video_ga...
The current trend in state-of-art LLM coding agents is giving more output, thinking longer and checking the results more to catch mistakes. Be it an economics inventive to make users burn through their quota or show increase in usage for shareholders, or a market demand of users liking the ability of models to do independent work without intervention or oversight; the result is what the article seem to call the Vibe Tax.
I myself asked Claude code recently to review a somewhat large PR, to see what it would find. I didn't expect much, but also didn't quite realize how the model would interpret my request; I burned $20 in 3 minutes in API usage, as it ran 2 sub-agents which themselves spun up 5 more each. Most sub-agents were manually checking for things clang-tidy would catch without actually calling clang-tidy. This behavior rose as i changed from sonnet/opus 4.6 to 4.8 and now 5.0.
I don't want to run a agent independently in this way; i ask targeted questions about specific things and review the result. But model development is targeted towards a more hands-off "vibe" workflow, because that's where the money and hype is. As a result, i find the models more frustrating, less trustworthy and more costly to my work. (I've even started using haiku more, since it remains to-the-point without steering away from what i ask)
That said, the companies are incentivized to sell you tokens, and therefore to have the models use as many tokens as they think you'll let them get away with for a given task / level of performance.
As if this wasn't bad enough, it also was not smart enough to regenerate the evidence in these contracts as it changed the underlying source code. So it would get in a loop where it would update code -> commit -> 15 minutes later CI would error citing the contracts weren't updated -> it would fix the contracts -> 15 minutes later CI would error because the fix was wrong -> it would fix the fix and commit -> 15 minutes later contracts would fail -> contracts were fixed again and this time maybe 30 minutes later it would pass, maybe it errors again.
This loop could go on all day every day if someone wasn't paying attention because the agent has no concept of time or wasted work. It's an AI livelock of sorts, but it will eventually converge in my experience. It'll just take 10x longer (literally like 20+ hours) than if you just intervene and tell it knock it off, so it feels like lighting money on fire (hence the tax).
That's why I feel like this vibe coding stuff has to actually be monitored, like a Tesla system -- because like a Tesla system it cannot be trusted to not crash into the proverbial code wall.
If the general idea is that these agents write too many tests, sure I guess? ‘Too many tests’ doesn’t sound like a failure case of engineering to me; typically software has had too few tests. Also, a lot of the power of these agents is their ability to self-verify and correct, which the test loop is a part of.
Nobody is making you pay this supposed tax. Just tell it not to write tests.
My agents have never created code that is straight-up garbage and I have never flushed a week's worth of tokens down the toilet. I just can't identify with all the constant complaints about AI-assisted coding.
And my biggest project isn't some hello world app. It's a self-hosted, privacy-focused personal financial management application that I intend to open source. It's about 126k LOC against 240k LOC of regression tests and 30k LOC of CI/CD pipeline. I'm doing 24x7 mutation testing on a dedicated box against the accounting engine and temporal systems. I even have specialized agents doing audits against Regulation Z (US banking law) criteria so the app models the required behavior of banks.
Most of my complaints about everything are nits, like the overly verbose and dense way LLMs communicate with me. Or their predisposition to add, add, and add more stuff when proper engineering practices are more often about subtraction (but I've built mitigation guardrails against a lot of that).
> a… personal financial management application… about 126k LOC against 240k LOC of regression tests and 30k LOC of CI/CD pipeline
Just how much functionality are you getting out of that? It's hard for me to imagine that people want that much out of such a program. I just keep a spreadsheet. (Yes, LibreOffice is also very bloated.)
> 126k LOC against 240k LOC of regression tests and 30k LOC of CI/CD pipeline
I mean...
Yeah, that's pretty self-explanatory why you don't identify complains about AI-assisted coding.
* the dev wants to describe an app in natural language then fall asleep while an AI works on it
* the dev wishes that the same AI would write less comprehensive tests
What exactly is a vibe coder to this dev?
(If you give claude an inch these days it'll just steamroll through a whole program of work without checking what it should be doing - a kind of overenthusiastic pull towards the first draft that is often detrimental and definitely wastes tokens, and even for very basic tasks it's using many more tokens than it should because it's doing this full belt and braces thing for everything, just in case you're an idiot).
But also... it's something you can easily reign in if you want to.
if i wanna write a web-app or python script; the models are better than ever. If i want to fix a specific bug in a established and trusted legacy cobe-base; Haiku 4.6 does a better job than Opus 5.0, because it does what it's told and nothing more.
The author wanted a todo-list starting-point; realistically 200 rows of html+CSS without the back-end. Heck, they may not even want to make a todo-app, but thought a todo-app would be a decent starting-point. So why would we ever want a model to spend a weeks worth of tokens on everything except the request the user asked? This is not a cost issue; this is a control issue.
Choose what makes you happy!