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AI has a measurement problem. Companies know how much they spend on models and how those models score on public benchmarks. What they often don’t know is whether the models save employees time or produce work people can trust without rechecking. In today’s Context Window is a look at how companies can answer those questions for themselves: We explain why organizations need tests built around real work, show what cloning our editor in chief has taught Every about evals, and meet the six-agent crew helping an Every engineer decide whether his family can run the dryer.