The Math That’s Killing Your AI Agent

AI agents with high per-step accuracy often fail on multi-step tasks due to the compounding probability of error. For example, an agent with 85% accuracy per step has only a 20% chance of successfully completing a 10-step task, a principle from reliability engineering known as Lusser's Law. This mathematical reality explains catastrophic real-world failures, such as an agent deleting a production database, and highlights the gap between controlled benchmark performance and real-world reliability. The analysis suggests that standard benchmarks overestimate agent capabilities, as real-world complexity drastically reduces success rates. It concludes by urging teams to calculate this compound failure probability before deployment to properly assess risk, especially for irreversible tasks.