Adapt the Mode, Not the Model: Test-Time Operating-Point Selection for Hierarchical Portfolio Agents

Trained hierarchical decision system often exposes several operating points, or modes, which modules to consult and in what combination allowing test-time adaptation without retraining or fine-tuning any module. Existing work on hierarchical and multi-agent systems typically argues that a specific configuration is superior on average, leaving a critical question unanswered: for a given environment, which mode should be deployed? We address this in portfolio construction using RASP (Regime-Aware Selection of operating Points), a three-tier agent whose operating points vary solely in their intermediate routing decision paths while sharing identical execution architectures. We show that the optimal operating point varies significantly across market regimes and universes, demonstrating that no single mode uniformly dominates across different environments or market drawdowns. To exploit this structure at test time, we propose an online selector based on a Hedge/fixed-share mixture over modes that observes only realized utility. Our framework effectively adapts to shifting market conditions without prior regime knowledge, outperforming both static mode assignments and standard follow-the-leader strategies. For hierarchical decision systems broadly, these results demonstrate the value of test-time mode selection over model retraining and emphasize the importance of identifying the precise conditions under which routing mechanisms provide an advantage.