This paper extends Rodney Brooks’ subsumption architecture into the era of Agentic AI by replacing its priority arbiter with a Generative Orchestrator that performs semantic mediation—interpreting heterogeneous agent outputs and integrating them into a coherent action rather than merely arbitrating among them. Brooks’ original model (1986) demonstrated that autonomous behavior can emerge from parallel reactive layers without symbolic representation, establishing principles later recognized as foundational to agentic systems: environmental responsiveness, autonomy, and goal-directed action. Contemporary Agentic AI, however, requires capabilities beyond mechanical response—decision-making, adaptive strategy, and goal pursuit. We therefore reinterpret subsumption layers as four interacting agent types: reflex, model-based, goal-based, and utility-based, coordinated through semantic mediation. The Generative Orchestrator employs large language models not for content generation but for decision synthesis, enabling integrative agentic behavior. This approach merges real-time responsiveness with interpretive capacity for learning, reasoning, and explanation. An autonomous driving case study demonstrates how the architecture sustains behavioral autonomy while generating human-interpretable rationales for its actions. Validation was conducted through a Python-based proof-of-concept on an NVIDIA platform, reproducing the scenario to evaluate and confirm the architecture. This framework delineates a practical pathway toward advancing autonomous agents from reactive control to fully Agentic AI systems capable of operating in open, uncertain environments.