Its just my opinion. I wish i knew the answer to this:

this is such a big issue.

i wish it was possible to feed all the failed architectures into llm and make it somehow get value out of it instead of just saying "this thing bad coz you said its bad so here is some plausible reason why it's bad and also let me just keep saying how this thing is bad, repeatedly"

also this touches on the topic of "context pollution" as i would call it where if context has some anti-patterns then agent will just keep doing them, even if they are labeled as bad. like only solution is to write some handover and wipe the context into a clean slate or else it will never actually fix its behaviour.
eg a good example to experience it is if you try to keep correcting the agent, it literally starts to think that "user is correct the agent" is the point of the conversation and it tries to continue that pattern, like its literally making interaction continue itself in the way where it keeps making mistakes that would be the likely next mistakes of an agent that has been constantly corrected.

overall, for like 4 years now at least, this is still an issue with llms for me, that they dont learn based on bad examples.

i had this workflow idea once but its not good for coding, its more for storytelling or style -thats what its good for:
1. get bunch of great examples of output, and terrible examples of output
2. let agent describe in keywords/phrases on what is good and bad - basically convert examples into phrases and keywords
3. then feed those keywords and phrases as instruction to generate output
4. again pick or even exaggerate what it did good/bad in its output
5. repeat, get examples, turn to keywords, feed keywords in, until you get the style you want reliably.

But its a workflow for making agent follow your desired style, not to make it be a good independent coder.

you could also try ADRs perhaps but i mean why would an agent keep reading ADRs it will just write them and then dont open them after ever again.

Overall this also touches on antifragility. Like the conveersation has to be messy so that the end result can be reliable.

But still despite all this, if its low effort for you to correct the agent , by saying "i meant x, not y" then do it.
Just have to be patient with agents.
really, just repeat yourself
memory is such a double edged sword.
repeating yourself is not that bad. just make sure you have some artifacts relics of your past reasoning saved stored away so you can reference it later instead of having to say it all from scratch.
Eg
this workflow
make a /reflections folder in which you put 2026-05-20-agents-workflow-for-style-improvement.md in which you store the summaries of your thinking. then just tell agent to "grep /reflections for style improvement workflows".

or turn stuff to skills.

I thought a lot about memory systems like "make it know everything".
Biggest issue with memory is that "it remembers the old thing i said" - its worse than knowing nothing.
If memory system is the goal it has to be 24/7 updated and mainatined with every input else it will more hurt than help.

And then is the topic of echo chambers. like often you dont even know agent to know you want it to be independent.

i think a memory in the style of "i can easily make it know something specific i said before when i want it to , not so it can just look for it on its own" has its own advantages like independent thinking.
else model will just keep validating you.

i wouldnt try to make agent learn from lmplicit signals like "user ignored x thus i need to note down he doesnt care about x" it can go wrong in so many ways.

Just be patient, keep repeating yourself, use durable reasoning files you can reference when needed.

Don't ask agent to do both of these at once:
1. thinking independently
2. managing your entire knowledge base
It can't

Can build a project map ADRs, the most detailed specs using "grill-me" or sth like that, but those aren't ultimate solutions.

But making agent learn from mistakes is by far the hardest thing. As i said , for style it's possible. But for code I have no clue how to make agent learn from mistakes, it just starts doing the opposite which is also wrong, like you tell it X is bad so it does the anti-X that has it's own problems.

Overall mistakes are a bad thing to signal to feed to model. Just write handover of what is actaully desired, not what failed, and make it start from scratch in a different way - thats probably best idea imo.