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AI agents are rapidly moving from experimental prototypes to production systems handling critical business processes. Research shows that even the best current AI agent solutions achieve goal completion rates below 55% when working with CRM systems, exposing a fundamental gap between demonstration capabilities and production reliability. Organizations deploying enterprise-wide AI

TL;DR AI agent reliability depends on continuous monitoring and proactive drift prevention. Multiple studies show that model performance almost always decays in production; one widely cited analysis reports that roughly 91% of machine learning models degrade over time, a phenomenon broadly referred to as drift. Essential prevention strategies include