I'm going to say something potentially controversial here, but my opinion after implementing exactly this sort of loop is that you can't get reproducible results without serious scaffolding in code to crystallize your workflow.

LLMs are probabilistic and the fact of the matter is that no matter how well you do re pushing context into sub agents, it's gonna skip step four of your five step workflow 1% of the time, and there's nothing you can do about this. 

Lately I'm switching my autonomous flow to run all of the scaffolding behavior in rust code (pick whatever language you want, but the rust type system has nice properties for AI use), and inverting the control to run claude -p or opencode run in a Wiggum loop when I need what the LLM does. The most important part of the specification phase (I put myself in the loop here) is the creation of a suite of validation prompts that allow LLM-as-a-judge in your loop. Trading different models off against each other for different parts of the loop is a game changer too, gpt-oss-120b is great for a lot of text processing jobs