Why Software Factories Fail (or: harness engineering is not enough)

Article URL: https://github.com/humanlayer/advanced-context-engineering-for-coding-agents/blob/main/wsff.md

Comments URL: https://news.ycombinator.com/item?id=49023019

Points: 369

# Comments: 260

Hacker News 讨论

369 points · 261 comments · 查看原帖

sathish316
I call it the Intent-Implement-Quality problem. Software factories can implement anything given a one-liner requirement. That one-liner requirement can be a complete app/product, epic, feature, bug, design change or refactoring. But these one liner requirements are requirements coming from a human who has an intent or requirement or direction for the product to evolve in mind. Can Software factories manufacture intent that reflects the exact requirements of the person using it or their vision of how the product or software should evolve? Implementation in any language is as easy as generating a summary for an LLM. If coding is only math and there is only one way to translate a requirement into an implementation, the problem boils down to just providing or verifying generated intent. But, there is definitely more than one way to implement a thing and one of the ways leads to a design and
fishtoaster
There's some good ideas and points in here, but this bit threw me: > # We tried this > In July 2025 we went full lights-off Isn't it pretty well-accepted at this point that the models underwent a step-change in usefulness around fall 2025 / spring 2026? I know that I was able to start handing agents whole features after that, but not before. I feel like any perspective/experience on "what agents can/can't do" from before that period is... maybe less than relevant to the modern era. TFA calls it out a few sections later with "But surely the models have gotten better since then", but then just writes off any improvement. That does not match my experience.
iamwil
I've been building and running my software factory for 8 months now. Granted, there's no automated pulling down tasks and pushing PR right now (soon!). But after specifying what I want, it mostly goes to shipping on its own. On occasion, it does raise issues that I have to make a decision on. After doing systems evals on review, I've stopped looking at code during review for 4 months now. I do spend a lot of time up front specifying what I want. My prompts aren't one-liners, but rather an interview process where we work through all the open questions and ambiguity. I haven't hit the wall that the OP talked about (when agents just can't seem to make the right changes, and it's impossible for me to go in and change things manually). I used a lot of guardrails such as plan reviews, browser-based QA, adversarial reviews, unit tests, linters, typecheckers, post-commit hooks, and formal method
janalsncm
Either you need to understand how your codebase works or you don’t. Claude can write the code for you but it can’t understand it for you. That part has to happen at human speeds. There are cases where you don’t have to understand everything, but I think that’s a more nuanced question. All of the above is true even if Claude writes perfect code.
stillpointlab
It is comforting to find other people experiencing the exact same reality as me, since I see so much in this post that matches my own experience. It reminds me of all the recent talk about "taste". Architecture "quality" may not be objective in a right/wrong sense, in the same way that fashion isn't right/wrong. It's like we are all going to have to relinquish reason/rationality to the machine and start to study up on aesthetics. Even historically, my big struggles have usually been deciding between two nearly-equivalent options. I get this a lot now with LLMs because there is no break in-between these decisions that implementation used to force. I feel I'm constantly making "taste" calls between tradeoffs that have no clear objective criteria, and it is as exhausting as the code review this post (and my experience) suggests are still necessary, even with Fable/GPT-5.6 level models. In m
rglynn
To me, the thing that stands out about the whole state we're in here is PR review. Yes, in an ideal world, PRs read well, are a joy to review, reflect what you discussed etc etc. We have to be real; there is only so much we can do to that end. I'm not sure how the best teams do PR review, from my perspective it sucks . I'm talking specifically about the UX. I've always hated Github's PR page, so I typically reviewed by pulling down the branch and opening the diff with $EDITOR. These days I think there's really no excuse for the awful UX. Linear (a company that isn't even in the domain of code review) put out a basic PR review feature[0] that is already better than what GH offers. It's simple: point a small model at the PR, group file changes together based on theme, add some commentary and sort by importance (schema changes > openapi spec). Immediately, so much mental load has been reduc
sgt101
This is interesting and all, but why should we believe any of it? 1) This guy has a track record (confessed) of making shit up, yapping on about it, and pushing it on the innocent. He's done a bunch of damage with his bullshit and now wants us to pay attention again. I mean - something something off fella. 2) There is NO EVIDENCE AT ALL that his ideas are good. He's just making stuff up. Give me a reason. Also shouldn't he be ostracised and stripped of his wealth for his previous rubbish?
firasd
I think there is a fundamental issue here of what building software even means If you think you can just assign Github tickets to AI agents and go drink daiquiris on the beach I think you'll find that you end up with more and more towers of abstraction and indirection. There are 'points of view' that emerge during coding I think. And at some point you as a human have to be like "wait... what if we use Redis here". "Wait.. the API is already returning the data we need". "Wait... let's not add customers to the report who have not been active in the past year". Stuff like that