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ADRS performance depends on component interactions that are poorly understood, expensive to explore, and (as we show) not well captured by standard convergence guarantees. These guarantees rely on structural assumptions that do not hold under the ADRS process we formalize. We introduce GAMBLe, a framework that decomposes ADRS behavior into four parameters (generator $G$, assessor $\\mathcal{A}$, discovery mechanism $\\mathcal{M}$, budget $B$) and one compositional object, the effective landscape $L_{\\text{eff}} = \\mathcal{A} \\circ G$, which reveals that distinct generator-assessor pairs induce structurally different per-problem optimization landscapes. 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