Agentic Drift Time Elapse Longevity

AbstractAs Large Language Models (LLMs) evolve from static request-response tools to autonomous "Agentic" systems, thestability of decision-making over extended temporal horizons has emerged as a critical failure mode. Currentliterature identifies "Agentic Drift" as a stochastic deviation from intent, but few studies quantify the correlationbetween "Contextual Bloat" and liability exposure. This study analyzes 5,000 unique interactions within a continuousautonomous workflow to measure the degradation of negative constraint adherence. We identify a specific "DriftThreshold" at approximately 45 minutes of continuous session time, where adherence to safety protocols degradesby 14% (p < 0.001). To mitigate this, we introduce the "Senate Architecture" (Patent Pending), utilizing a Multi-ModelConsensus mechanism to enforce a forensic Chain of Custody. By leveraging "Synth"—a proprietary 7-centered LLMsystem for retaining probative decision nodes while discarding non-essential token history—we demonstrate a 99.2%reduction in drift over the same 6-hour period.