Study finds AI agents fail via silent drift

An analysis of more than 100 production AI agents shows that they typically fail through silent drift rather than sudden errors. The author argues that problems with output quality, cost, and latency surface within days, while explicit errors are the slowest indicators to appear. Furthermore, issues such as runaway tool loops tend to be chronic and closely linked with sudden spikes in token spend. Open article →