I wish people would describe in more detail the tasks they use LLMs to code. My experience is that simple components in an existing architecture are fine, but anything requiring architectural considerations quickly becomes a mess. On my projects (e.g. a ui framework), running multiple agents in parallel would just increase the speed at which it can stuff up the project.

I get this question a lot, and I found it hard to answer briefly, so I ended up writing a longer post about how I work:

https://www.trigosec.com/insights/mob-programming-for-one/

The short version is that I don’t let AI agents work unsupervised on my code. I treat them like participants in a mob programming session instead of autonomous developers. Different agents get different roles (implementer, reviewer, architect, security reviewer, etc.), and I stay involved throughout the process.

I also agree with your point about architecture. Generating isolated components is relatively easy; preserving and evolving the architectural boundaries across a larger codebase is much harder.

We’re still missing a good way to express and measure architectural quality. Until then, architecture heavy work requires much closer supervision than implementation heavy work

I built this with 94% written by coding agents: https://buildermark.dev/

The complete log of all prompts and commits is here: https://demo.buildermark.dev/projects/u020uhEFtuWwPei6z6nbN

I used LLMs to develop Whistle Enterprise (https://whistle-enterprise.com) from the ground up, from scratch.

It's taken _a lot_ of time and effort, but this is an example of what can be developed using LLMs alone.

You have to have dedication and a goal to reach, but you can absolutely build anything if you're building with the right foundations in mind.

I personally limit LLMs to single files only at the moment. Self-contained components.

Using LLMs in a larger scope can sometimes work, but it has the real risk of turning a project into a mess after which you will have to undo the work and lose a lot of time.

Also, using LLMs this way with less clear boundaries will make reading and maintaining the code more cumbersome.

Me when not trying to meet management expectations, only as smarter code completion, formatting code, basic code analysis, and helping copy pasting code examples between languages.

Me when meeting management expectations, agent orchestration tools like Boomi and Workato calling into tools, doing with AI what a few years ago would be done with BPEL.

You have to make those architectural decisions and feed them to the agents. Be very specific. That's been my experience.

I found that this guys stuff has really helped me:

https://youtu.be/-QFHIoCo-Ko?is=FYYdukWluYX3vdQL

Worth a watch.

It's great for people who are just maintaining something. Less so for someone building something from scratch, in the earlier phases.

There are hour long youtube videos where people explain the process by using a complex toy project. Search for them.