Appiah Prince , Vijayalakshmi Kakulapati 3 4 (October-December) A deployed agent rarely breaks all at once A model update a changed tool or a shift in what users ask for can raise its violation rate a little at a time and nobody notices until the damage is done We ask how quickly different change detectors notice such drift when each is set to the same falsealarm rate We compare a CUSUM a likelihoodratio CUSUM an EWMA chart a rollingmean threshold a slidingwindow test and an ADWINstyle adaptive window on streams of pertask violation indicators with every detector calibrated to one false alarm per 1000 tasks and checked on fresh data For a step increase from 5 to 10 the CUSUM family the EWMA chart and the adaptive window all needed about 69 to 75 tasks median the rolling mean 79 and the slidingwindow test 120 On bursty drift the gap widens to 48 times For a slow ramp the classical firstorder delay formula was wrong by up to 9 times We derive a better one treating the CUSUM as a reflected random walk with slowly changing drift and it predicts the simulated median delay within 8 over a hundredfold range of slopes Estimating the baseline from 500 tasks raised false alarms by up to 139 times for detectors that use it and benign seasonality of 100 amplitude raised them about threefold All detectors ran in under 2 s per observation All results are simulationbased Copyright (c) International Journal of Computational Science and Engineering Research . All rights reserved 2026