Not directly relevant to the post being discussed, except as an example of how runaway automation can lead to unintended and large harmful consequences.

Wondering if you are referring to adherence to required data in a schema when you say LLMs do better with json vs markdown, or something else? Or perhaps to tool calls and/or strict json output being more reliably formatted for clean extraction?

Reconstruction of reasoning from scratch can happen in some legacy APIs like the OpenAI chat completions API, which doesn't support passing reasoning blocks around. They specifically recommend folks to use their newer esponses API to improve both accuracy and latency (reusing existing reasoning).

A simple example: can you arbitrarily manipulate the historical context of a given request to the LLM? It's useful to do that sometimes. Another one: can you create a programmatic flow that tries 3 different LLM requests, then uses an LLM judge to contrast and combine into a best final answer? Sure, you could write a prompt that says do that, but that won't yield equivalent results.

These are just examples, the point is you don't get fine control.