The Architectural Framework of Agentic AI 2.0

Maintaining coherent agentic work becomes an architectural challenge as information, participants, commitments, execution components, and operating conditions change. This paper proposes Agentic AI 2.0, a system-level architectural framework that integrates established concepts to organize ongoing goal-directed work in foundation-model-based systems. Six constitutive properties characterize this organization: Maintained Process State, Collective Coordination, Shared Operational Knowledge, Governed and Grounded Action, Computational Continuity, and Feedback-Driven Adaptation. The framework connects these properties to role-bearing agents, shared operational objects, interface contracts, and interacting execution, coordination, and adaptation loops. A composition guard specifies how local acceptance decisions connect across operational boundaries, while a criterion-level codebook supports configuration-specific assessment from explicit evidence. We examine the framework through a constructed production-order trace, disruption and handoff branches, counterexamples, and static source profiles of three application configurations and one infrastructure boundary case. The analyses distinguish local feasibility from joint commitments, effect reconciliation from execution-right control, and retained skill versions from governed activation and reuse. They also separate scoped negative evidence from unresolved evidence when diagnosing architectural relationships. Candidate evaluation and later reuse remain conditional in the worked example. Prospective profiles for enterprise productivity, adaptive manufacturing, and distributed mobility identify domain-specific evidence needs and evaluation priorities. The contribution is a traceable basis for architectural analysis, comparison, and design. Independent assessor studies, comparative diagnosis, and controlled and longitudinal implementation studies are needed to establish its practical value and the relationship between architectural choices, operational outcomes, and cost.