Long-horizon reasoning is where production LLM agents tend to quietly break. A model can produce a plausible-looking chain of thought, accept a wrong intermediate answer, and continue building on that error for every step that follows. By the time the final output appears, the damage is compounded and invisible. The paper behind ReFlect ( arXiv:2605.05737 , May 2026) quantifies exactly how bad this is: in controlled experiments, LLMs wrongly accept incorrect answers at least 76% of the time when using standard prompt-level self-critique — the "check your work" approach most developers reach for first. ReFlect proposes a different model. Instead of asking the LLM to critique itself (which mostly produces formulaic acknowledgment templates rather than actual error signals), it inserts a deterministic harness between steps — an external wrapper that checks for numerical inconsistency, grounding failures, and logical contradictions. No fine-tuning. No model changes. Just inference-time scaffolding.…