ABSTRACT. Temporal Synthesis studies the automatic synthesis of interactive programs (technically called strategies) from declarative specifications expressed in temporal logic. In this talk, we show how Temporal Synthesis provides a principled foundation for strategic reasoning in autonomous AI systems, leading to what we may call Symbolic Coding Agents. Symbolic Coding Agents use temporal synthesis to generate symbolic code, including strategies, guard rails, and decision-making monitors, thereby grounding deliberative behavior in logical specifications. The key to this research path lies in the rich body of concepts developed in reasoning about actions and planning, combined with a precise treatment of nondeterministic environments and temporal objectives. In such settings, plans must be treated as strategies rather than being blurred with individual execution traces, and goal satisfaction evolves during execution rather than being reducible to reaching states with fixed properties. These features are naturally captured within the temporal synthesis framework underlying Symbolic Coding Agents. Technically, we focus on synthesis from Linear Temporal Logic on finite traces (LTLf). LTLf specifications compile into deterministic finite automata (DFAs), which can be viewed as two-player game arenas, yielding efficient and scalable synthesis procedures. We then lift these finite-trace results to infinite traces through the Manna–Pnueli hierarchy, introducing LTLf+ and its obligation fragment while largely preserving algorithmic simplicity. Finally, we move beyond synthesizing individual strategies to analyze the space of all strategies satisfying a specification. By characterizing the set of compliant execution traces, without assuming individual strategies to be analyzable, we provide formal foundations for Symbolic Coding Agents to synthesize guard rails and decision-making monitors, as well as tools for responsibility attribution in agentic AI systems composed of multiple agents that make decisions independently.