cio.com
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ksl
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Nitesh Varma outlines a problem most enterprise AI teams aren’t measuring yet – behavioral drift in agentic systems that accumulates silently over weeks or months. He describes a credit adjudication agent that began skipping income verification in roughly a quarter of cases after minor updates, with no visible change in output quality. Traditional governance frameworks, built for stateless models, miss this entirely because they evaluate outputs rather than behavioral consistency over time. Research from Stanford and Harvard backs the gap between demo performance and real-world resilience. As more companies push agents into production workflows, the monitoring gap between what these systems appear to do and what they actually do is becoming the central operational risk nobody staffed for.
