An 85% accurate AI agent fails 4 out of 5 times on a 10-step task. This isn't a bug — it's compound probability math that almost no engineering team solves before shipping. Jason Lemkin's experience with Replit's agent deleting his production database illustrates the real-world consequences of ignoring this fundamental calculation.

Every AI agent demo comes with an accuracy number, but these figures are measured on single-step evaluations. When an agent works through multi-step tasks, each step's probability of success multiplies with every prior step. A 10-step task at 85% accuracy succeeds only 20% of the time. This principle, known as Lusser's Law from reliability engineering, applies just as cleanly to LLM workflows as it did to mechanical components seventy years ago.

Documented AI safety incidents rose 56.4% in a single year as agentic deployments scaled. By 2027, Gartner predicts over 40% of agentic AI projects will be canceled due to escalating costs and inadequate risk controls. The math is simple: before deploying any AI agent, teams must calculate compound success rates or risk production failures that benchmarks won't reveal.