Learning Before Failure: Preventing Failure by Learning What Went Wrong and Why

When people learn from a failure, whether their own or someone else’s, a useful lesson captures why it happened, what correction the evidence supports, where that correction applies, and how soon they must act. Can explicitly checking when a lesson applies help turn prior experience into timely preventive action? We introduce Learning Before Failure (LBF), a framework for turning stored lessons into timely decisions in language agents, and test one implementation, LBF-Gate. Before each decision, the gate checks lessons against the current situation and marks applicable lessons as rules to follow. The full lesson bank remains readable, and the language model chooses the action and its timing. In a constructed flood search-and-rescue environment, we compare LBF-Gate with an agent that reads the same structured lessons unmarked and with one that stores model-written free-text reflections on prior failures. The first comparison holds lesson content fixed to distinguish what is remembered from how it is used. Model weights and stored lessons remain frozen during testing on new missions. Across 375 independent blocks per recurrence class per model, LBF-Gate reduces normalized held-out loss against the same lessons unmarked by 0.108 on GPT-5.4 mini and 0.087 on Claude Sonnet 4.6. Every class-specific lower bound is positive on both models, and loss increases on cases requiring a different correction or no intervention remain within the prespecified tolerance. LBF-Gate also outperforms free-text reflection. A separately prespecified control study shows that marking the wrong lesson substantially reduces the benefit; on GPT-5.4 mini, wrong marking performs worse than leaving lessons unmarked. The gains cannot be explained by shorter inputs or by marking an arbitrary lesson. Preventive reuse must therefore be evaluated both for timely action on relevant lessons and for the consequences of applying lessons where they do not belong.