AI agents can falter by 16 steps despite strong short runs, a paper summary says

A post summarizing the Microsoft paper “How Fast Do Agents Rot?” says success usually dropped across nine models as the number of dependent steps increased. It describes small errors compounding through longer workflows, exposing failures that short benchmarks miss. The post recommends testing agents at realistic workflow lengths, measuring per-step reliability and adding checks or checkpoints before a bad step affects everything that follows—rather than treating a benchmark pass rate as proof of production readiness.