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That's the failure to automate. The AI isn't telepathic, so agentic engineers not automating this stuff is skipping out on the engineering part.

You setup the environment and then you do the work. Unless you are switching employers every week, you invest in writing that stuff down so the generation is right-ish and generate validation tooling so it auto-detects the mistakes and self-repairs.

by mittensc2 hours ago|

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sometimes you write the feature and write it well so it's reusable.

imagine you have to implement a specific algorithm for a quantum computer.

There's no value setting up AI to do the writing for you. That might be orders of magnitude harder then writing the algorithm directly.

For highly specialized one-off features, it doesn't always pay off.

On the other hand, if all you do are some generic items that AI can do well... then I'm not sure you're going to have a job long term, your prompts and automation will be useful for the new junior hires that will be specialized in using these and cost effective.

by timschmidt7 hours ago|

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I think there's a level above that where the words to describe such structure are familiar and readily available and hey guess what? The model understands those too. Just about every pattern has a name. Or a shape. Or an analog or metaphor in other languages or codebases. All work as descriptors.

by necovek5 hours ago|

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This presumes that most of this stays encoded as words in our brains: the effort to translate some of these into words might be similar to translating it into code (still words, just very precise).

It's like talking legalese vs plain English; or formal logic vs English. Some people have the formal stuff come more naturally, and then spitting code out is not a burden.