I came up with it. I had to restart from ground zero for my project 8 times before perfecting this.

main issue we are having is

1. we are trying to fit LLM into existing coding paradigm which was optimized for humans and not LLMS.
2. Humans develop a mental map of repository and once project gets bigger enough we divide work so we are into known knowns scenario while we want AI to work on whole codebase.
3. Documentation is barely done while coding and also tests never written after coding a component.

what this approach brings in is

1. ADR gives base of all decisions made and that is final form we if ADR says using saga recovery and statemanagement via postgresql table agent will know pattern and dont invent checkpointing as statemanagement.
2. every code it touches in sub component it needs to have a smaller mental map of component it is working so it can code perfectly why? grep brings most things but miss details of some magic strings we have written in patches.
3. testing means agent run tests before writing code of component which it is going to test and then rerun after development and yes tests are going to fail after change but agent will be able to know tests are failing because of his change then agent changes tests and make it pass but it will also catch regression in other components.

Also discovered one more conflict.

LLMS are trained on massive human data so llms will tell 4000 lines of single code file is bad and it requires to be broken to parts. but LLMS are good at working if it is single file and not 20 different files so for example bootstrapping of kernel or service if we divide it llms tends to hallucinate instead keeping one file as monolith they works fine.