This chapter outlines future directions for extending SAGE into a general architecture for sentiment-responsive macroeconomic analysis. Key theoretical developments include introducing heterogeneous agents, overlapping generations, financial intermediaries, and feedback mechanisms between knowledge and sentiment. These would allow SAGE to capture asymmetric exposures to expectations, the endogenous dynamics of inequality, and cross-cohort or network-based contagion. Empirically, the chapter proposes strategies for estimating sentiment functions, calibrating asset utility parameters, and testing predictions on portfolio shifts, labor supply, innovation, and macro-financial instability. SAGE is also shown to apply across emerging domains where uncertainty, intertemporal coordination, and expectations formation are central—such as climate transition, artificial intelligence, digital monetary systems, and global financial interdependence. In these contexts, sentiment is not noise but structure: it shapes liquidity, investment, and risk in ways that are regime-dependent and policy-relevant. Methodologically, the chapter calls for embedding SAGE into stress testing, early warning, and simulation platforms. Ultimately, SAGE is positioned as an open and extensible system—combining cognitive realism, institutional grounding, and dynamic portfolio logic—to help reorient macroeconomics toward the informational, behavioral, and systemic realities of the twenty-first century.