Empirically model multi-stage pipelines to analyze why companies adopt autonomous AI agents: latency compression, cognitive toil elimination, surge capacity, and net unit economics.
In multi-stage enterprise workflows, tasks traverse sequential stages, where each agent processes an item or escalates failures to human operators. Cumulative straight-through autonomy compounds as the product of each stage's pass rate, while remaining tasks trigger human review. Cycle time plummets from forty minutes of human effort to just over three minutes, eliminating thousands of routine hours each month. In the simulator, dragging volume or autonomy dynamically recalculates net savings after accounting for agent API inference costs.