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Artificial intelligence systems increasingly operate as agentic systems, exhibiting consequential decision-making authority. As AI proliferates into multi-agent federations, silicon-embedded networks, and combinatorial configurations we term "Agentic+," we lack a foundational discipline for governing this expanding frontier. This paper introduces Agentic Governance Architecture (AGA): a unified framework for designing, governing, and harmonizing systems where agents—human or artificial—interact with consequential autonomy. AGA addresses three critical challenges: (1) behavioral drift across distributed agent networks, (2) accountability gaps in autonomous decision-making, and (3) the absence of shared governance substrates that ensure coherent system evolution. Drawing on insights from distributed systems, organizational design, cybernetics, and AI safety, we present architectural principles, invariants, and protocols that enable agentic systems to scale safely while maintaining human oversight and control. This work establishes AGA as a discipline bridging engineering, policy, and ethics—providing practitioners with practical frameworks for building resilient Agentic+ ecosystems. We synthesize foundational concepts from cybernetics (Wiener, Ashby, Beer), systems theory (von Bertalanffy, Simon), governance studies (Ostrom), AI safety (Amodei et al., Russell), and complex systems analysis (Perrow, Leveson, Hollnagel) to articulate a coherent approach to agentic governance that addresses both technical and institutional dimensions of autonomous system coordination. This is Paper 1 of a 5-paper series titled "AI Governance: Maturing Foundation Discipline." Subsequent papers address drift mitigation mechanisms (Part II), network-on-chip inspired architectures (Part III), relational vector adjacency (Part IV), and cognitive substrata architecture beyond RAG (Part V).