This chapter presents a consolidated case study that demonstrates the practical instantiation of Agentic Artificial Intelligence (AI) in a real-world environmental monitoring context. Building on the conceptual, theoretical, and architectural foundations established in previous chapters, the chapter describes the design and operation of an agentic system deployed to support long-term analysis and decision-making in a complex natural environment. The case study illustrates how agent roles, coordination mechanisms, and learning dynamics are integrated within a unified system architecture. Particular attention is given to how reinforcement learning enables adaptive behaviour and how language-based interpretability supports transparency and human interaction. By examining system behaviour, adaptation processes, and observed outcomes, the chapter provides concrete insights into the practical challenges and benefits of deploying agentic systems in dynamic, real-world settings. The discussion highlights how Agentic AI enables persistent autonomy, collaborative reasoning, and meaningful human oversight, offering a tangible reference point for evaluating the applicability of agentic approaches beyond theoretical constructs.