Failure Surface Mapping

Failure Surface Mapping treats evaluation as a bounded engineering exercise: before writing a single scorer, enumerate every place an LLM-powered workflow can break, then rank each by client-visible blast radius. Voice agents fail differently from retrieval pipelines, which fail differently from autonomous tool-calling loops. Cekura simulates thousands of personas to expose interruption and gibberish failures in voice before launch, while Agnost AI mines live conversations for frustration loops and repeated retries that synthetic tests miss. The framework matters because agencies bill for reliability, not for eval coverage. A retainer client tolerates a slow dashboard refresh but not an agent that leaks a competitor's pricing into a chat reply. Mapping the surface first tells you which 20% of failure modes justify continuous monitoring and which can wait for a quarterly review. The output is a one-page risk register per client deployment, priced into the retainer as production assurance.