Generative AI is redefining the foundations of software architecture—not as an add-on capability, but as an organizing principle. The challenge is no longer merely generation, but designing systems that harness generative capabilities intuitively, adaptively, and at scale. Agentic-AI should be understood as a foundational architectural shift—where generative models are not simply integrated into software, but where software is built around GenAI—intelligence—from the ground up. Traditional software has focused on structuring information systems through deterministic logic and rigid abstractions. But natural intelligence operates differently: probabilistic, fuzzy, and adaptive by design. This shift calls for a rethinking of architecture—one that moves beyond static flowcharts and toward principle-oriented design. By identifying and defining robust architectural principles, conditions are created in which intentional fuzziness and creative emergence become drivers of new levels of capability and performance. Application environments such as autonomous research, code generation, and vibe-coding exemplify where Agentic-AI must deliver: enabling runtime reasoning, contextual control flow, and systems that evolve through use.