I've set a few rules for working with coding agents:

1. If I use a coding agent to generate code, it should be something I am absolutely confident I can code correctly myself given the time (gun to my head test).

2. If it isn't, I can't move on until I completely understand what it is that has been generated, such that I would be able to recreate it myself.

3. I can create debt (I believe this is being called Cognitive Debt) by breaking rule 2, but it must be paid in full for me to declare a project complete.

Accumulating debt increases the chances that code I generate afterwards is of lower quality, and it also feels like the debt is compounding.

I'm also not really sure how these rules scale to serious projects. So far I've only been applying these to my personal projects. It's been a real joy to use agents this way though. I've been learning a lot, and I end up with a codebase that I understand to a comfortable level.

While this is a legitimate set of rules to follow for maintaining code sanity and a solid mental model of how a codebase may grow, it’s always challenging to stick to them in a workplace where expectations around delivery speed have changed drastically with the onset of AI. The sweet spot lies in striking a balance between staying connected to the codebase and not becoming a limiting factor for the team at the same time.

I was trying to follow similar rules, until one day I had to solve a hard mathematical problem. Claude is a phd level mathematician, I am not. I, however, know exactly the properties of the desired solution and how to test it’s correct. So I decided to keep Claude’s solution over my basic, naive one. I mentioned that in the pull request and everyone agreed that was the right call. Would you open exceptions like that in your rules? What if AI becomes so much better at coding than you , not just at doing advanced mathematics? Would you then stop to write code by hand completely since that would be the less optimal option, despite you losing your ability to judge the code directly at that point (and as in my example, you can still judge tests, hopefully)? I think these are the more interesting questions right now.

I’ve also heard it being called “comprehension debt,” which I like a little more because I think it’s more precise: the specific debt being accrued is exactly a lack of comprehension of the code.

This is great until the "gun to your head" is your skip-level manager demanding that a feature be implemented by the end of the week, and they know you can just "generate it with AI" so that timeline is actually realistic now whereas two years ago it would have required careful planning, testing, and execution.

I agree to this though it also depends on the nature of project.

Had a project idea which I coded with the help of AI and it became quite large to a point I was starting to have uncharted areas in the code. Mostly because I reviewed it too shallow or moved fast.

It was a good thing as that project never floated but if I were to do such a thing on my breadwinning project I would lose the joy.

I just had a Claude episode. Instead of trying to fix the bug, it edited the data to hide the bug in the sample run. This kind of BS behavior is not rare. Absolutely, if you do not understand every bit of what's going on, you end up with a pile of BS.

This is about how I use it. I initially use it to carve out an architecture and iterate through various options. That saves a lot of time for me having to iterate through different language features and approaches. Once I get that, I have it scaffold out, and I go in and tidy things up to my personal liking and standards. From there, I start iterating through implementations. I generally have been implementing stuff myself, but I've gotten better at scaffolding out functions/methods through code instead of text. Then I ask it to finish things off. That falls into your first category of letting it implement stuff that I already know I could do. Not sure if it's faster. But it's lower cognitive load for me, since I can start thinking about the next steps without being concerned about straightforward code.

This all works pretty great. Where it starts going off the rails is if I let it use a library I'm not >=90% comfortable with. That's a good use of these tools, but if I let it plow through feature requests, I end up accumulating debt, as you pointed out.

For my uses, I'm still finding the right balance. I'm not terribly sure it makes me faster. What I do think it helps with is longer focused sections because my cognitive load is being reduced. So I can get more done but not necessarily faster in the traditional sense. It's more that I can keep up momentum easier, which does deliver more over time.

I'm interested in multi agent systems, but I'm still not sure of the right orchestration pattern. These AI tools still can go off the rails real quick.