A reflection on how AI coding harnesses and LLMs are accelerating the problem of 'messy reasoning horizons' — analogous to layered soil horizons in codebases — where flawed reasoning layers compound into catastrophic decisions. The author shares a concrete example of an incident analysis that nearly led to solving a non-existent problem due to unvalidated LLM output. Five practical approaches are proposed: making no assumptions about any reasoning context, ensuring authors are the first human reviewer of their own output, prioritizing deterministic software conventions, separating experimentation from production work, and structuring LLM use to exploration and refinement rather than direct conclusions.
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