Traditionally, communication has been treated as the reliable transfer of data between entities, with the goal of reproducing messages as faithfully as possible, independent of meaning or downstream utility. While effective in classical systems, this data-centric paradigm becomes limiting in modern intelligent environments involving distributed decision-making, multi-agent interaction, and large-scale coordination, where what matters is not only what is transmitted, but what is meant and how it is used. In this talk, we focus on semantic communication protocols as a general paradigm for information exchange in intelligent systems, with applications spanning wireless networks and beyond. These protocols go beyond symbol fidelity to enable meaning-aware, task-driven interaction under strict resource constraints, operating over shared latent structure rather than raw data. We discuss how such protocols can be learned and optimized, and how they naturally extend across a broad range of applications, including agentic AI systems, where coordination, reasoning, and adaptation emerge through interaction rather than explicit design. We present a unified perspective grounded in artificial intelligence, causal reasoning, game theory, and theory of mind, where communication, inference, and control are jointly learned processes. Within this framework, we highlight three directions: emergent semantic communication via signaling games and neuro-symbolic reasoning, where efficient protocols arise from interaction; causal semantic communication in digital twin and wireless network systems that exploits invariances for robust control; and multi-agent world models with metacognitive reasoning, where agents infer and adapt to others’ beliefs and intentions without explicit communication, leading to emergent collective behavior. Collectively, these results point toward a shift from engineered communication protocols to learned, adaptive, and emergent semantic interaction systems, where meaning and reasoning become first-class design principles.