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Most universities have responded to AI by rewriting assessment policies and running prompt-writing workshops. Context engineering demands something different: infrastructure decisions that commit institutions to a direction. This post explains what context engineering involves, why it matters for health professions education, and why the gap between changing words and changing structures is where most institutions are stuck.
When AI agents consume documentation as operational input, it undergoes a category shift from reference material to operational architecture — inaccuracies no longer merely inconvenience readers, they cause system failures. This essay argues that the primary bottleneck for institutional AI integration is not AI capability but information architecture: how institutional knowledge is structured, maintained, and made available to AI systems. Documentation written for human readers cannot function as reliable AI input without deliberate restructuring around explicit relationships and rigorous maintenance workflows. Treating this transition as a governance imperative — rather than a technical afterthought — determines whether AI integration delivers on its institutional promise.