Keep AI systems correctable as feedback accumulates. Govern knowledge promotion and withdrawal, protect authorization boundaries, reconcile unknown execution outcomes, and test the conditions that should stop the workflow.
Evaluate infrastructure claims against scoped evidence. Use a two-cluster example to separate supportability, capacity, isolation, and resilience, including the difference between normal and failure-state capacity.
Separate feedback collection from changes to memory, runbooks, and model weights. Govern each update with provenance, evaluation, release controls, and a practical way to withdraw a mistaken lesson.
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