The aging process represents one of biology's most complex system-level phenomena. A major challenge is moving from observing its correlates to identifying its fundamental, targetable bottlenecks. In this talk, I will explore a reverse-engineering approach, using pharmacological interventions in model organisms to deconstruct the mechanisms of aging and pinpoint promising avenues for intervention.  I will discuss how we can leverage existing biological data and what new, targeted measurements are required to fill critical gaps. A key question is the selection of appropriate model organisms that offer the right balance of physiological complexity, experimental tractability, and translational relevance for aging research. Furthermore, I will examine the role of artificial intelligence in this endeavor: while AI excels at finding generalizable patterns, its success is critically dependent on the quality and nature of the underlying data—an area where significant improvements are needed.  I will present examples from my work across multiple species, including the development of a scalable high-throughput platform for pharmaco-biology in Daphnia. This system allows us to characterize drug-induced perturbations and link them to lifespan and healthspan outcomes. We will discuss a computational framework to regress macro-phenotypes to the molecular pathways. Finally, I will outline central challenges in the field and propose concrete directions for researchers interested in joining the effort to reverse engineer aging.