LLM agents fail in four predictable, mechanism-level ways. Attention decay, reasoning decay, sycophantic collapse, hallucination drift. The current stack (prompting, fine-tuning, RAG, agent loops) cannot close them because each layer operates inside the same decaying chain. The fix is an external layer we call a reasoning harness. If you have built an agent that runs more than ten steps, you have watched it drift. Plans fragment. The system prompt you wrote at the top of the context stops binding by turn thirty. The model agrees with whatever you push back on. A confident answer papers over a retrieval call that returned an ambiguous result. These failures are not random, and they are not artifacts of model size. They are not going to be fixed by the next checkpoint. They are predictable consequences of how transformers compute and how post-training shapes them. Four distinct mechanisms, each with a specific architectural cause.…