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Smart drug delivery systems promise to transform therapy by linking drug release to physiological need, local microenvironmental cues, or algorithmic feedback. Their ambition is not merely to administer medicines more conveniently, but to create therapeutic platforms that sense, decide, and act. Yet this promise remains vulnerable to sensor errors, biological noise, material instability, actuator failure, and unpredictable patient behaviour. The dominant design philosophy in smart delivery has been shaped by precision, specificity, and near-perfect triggering. Systems are often evaluated as though the correct signal will be detected, the intended release pathway will activate, and the therapeutic response will follow the modelled trajectory. This assumption makes many platforms appear elegant in controlled studies but fragile in messy clinical environments. This perspective argues that smart drug delivery needs a failure-tolerant design paradigm. Rather than treating malfunction as an exceptional event to be eliminated, failure-tolerant design treats drift, delay, degradation, and misclassification as expected operating conditions. The aim is not to abandon precision, but to make precision recoverable when the system deviates from its intended state. The framework proposed here integrates control logic, risk engineering, and pharmaceutical performance principles. Control logic supplies feedback, fault detection, and adaptive recovery; risk engineering supplies structured failure anticipation and mitigation; pharmaceutical performance anchors every decision in pharmacokinetics, pharmacodynamics, material stability, and patient use. Together, these domains can move smart drug delivery beyond the brittle ideal of error-free function. The central claim is that smart drug delivery systems should be designed to fail intelligently. A clinically useful system must detect its own unreliability, degrade toward a safer state, activate independent recovery pathways, and preserve therapeutic performance within acceptable bounds. Such a shift would require new engineering practice, new regulatory expectations, and a more honest understanding of biological variability.