Why Wall-Clock Monitors Trap AI Agents in a Constant Alarm Loop — and the Flaw That Makes It Inevitable

A new arXiv paper by Manvendra Modgil, "Bistable by Construction: Wall-Clock-Calibrated State Monitors Have No Moment-Detection Regime at Agent Cadence," corrects a previously reported "State Saturation Trap" and exposes a fundamental design flaw in runtime monitors for autonomous agents. The earlier finding, based on threshold-on-state triggers over a continuous affect engine, showed near-constant alarms on SWE-bench debugging agents. A post-release audit revealed the engine received dt=0 between actions, so the exponential decay never operated—making it a pure-accumulator result. Modgil treats this flaw as an experiment and discovers that the key variable is whether a monitor's dynamics are calibrated in sample time (per observation, like CUSUM) or wall-clock time (half-lives in seconds, like affect models and EMA baselines). On agent streams where inter-action time varies by orders of magnitude, these calibrations diverge catastrophically.

In a pre-registered sweep over uniform dt intervals (0 to 600 seconds) on 20 trajectories, the wall-clock level trigger exhibits two regimes: at dt<=1s, constant alarms (20/20 trajectories, median 18 firings); at dt>=60s, completely silent. Every critical dt lies in the (1,30]s range. Real agent coding runs measure latency at median 1.53s (p90 2.33s)—sitting squarely inside the trap regime, vindicating the empirical finding under a corrected mechanism. Modgil shows this structure is a property of the calibration class, not the engine: a minimal wall-clock accumulator over the raw error stream reproduces the same cliff, while a sample-time CUSUM over the identical stream is exactly dt-invariant (20/20 trajectories). A rising-edge trigger with hysteresis fires 0-3 times per trajectory in every condition, escaping the trap but not recovering human intervention timing. The paper concludes that wall-clock-calibrated leaky-integrator monitors admit no regime in which they act as moment detectors on agent streams—a critical insight for agent safety and debugging.