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Description
AI is moving beyond static models and one-off evaluations into systems that continuously adapt, learn, and operate autonomously, often inside real environments, not just test datasets. Most teams still rely on benchmark-driven evaluation, which is useful, but increasingly misleading. It tells you how a model performs at a single point in time, not how it behaves once it’s deployed and interacting in real environments. And that’s the problem: the environment itself changes, adapts, and evolves, so AI must operate in a world that never stands still.
