Not sure I'm fully following your question, but maybe this helps:

IME deep thinking hgas moved from upfront architecture to post-prototype analysis.

Pre-LLM: Think hard → design carefully → write deterministic code → minor debugging

With LLMs: Prototype fast → evaluate failures → think hard about prompts/task decomposition → iterate

When your system logic is probabilistic, you can't fully architect in advance—you need empirical feedback. So I spend most time analyzing failure cases: "this prompt generated X which failed because Y, how do I clarify requirements?" Often I use an LLM to help debug the LLM.

The shift: from "design away problems" to "evaluate into solutions."