Assured Autonomy is a novel area that merges Artificial Intelligence (AI) and Formal Methods (FM), concerning building AI agents that autonomously deliberate how to act in a changing, incompletely known, unpredictable environment under formal guarantees. A popular specification language for describing formal guarantees is Linear Temporal Logic (LTL) from FM. However, LTL is interpreted over infinite traces (relating to non-terminating systems). Since AI agents are not dedicated to a single task in their complete life cycle, but are supposed to accomplish one task after another, AI applications often employ a finite-trace variant of LTL, denoted as LTLf. In particular, the study of LTLf synthesis brings the intellectual merit of achieving Assured Autonomy by allowing agents to automatically construct programs with guarantees to meet their tasks specified in LTLf. In this talk, I will review an evolving journey toward Assured Autonomy through LTLf synthesis. Starting from an attempt to devise a symbolic backward LTLf synthesis framework, which has demonstrated its significant efficiency in various applicable scenarios, the journey evolves into a forward LTLf synthesis technique that highlights several interesting avenues of future work in the context of Markovian Decision Process.