基于地理空间数据运行的智能体 AI 系统不仅需要自主推理和工具使用，还必须支持显式编排、空间验证、治理、溯源、故障处理以及人类决策权。前期工作提出了 ANASSA，这是一个面向空间智能的智能体 AI 编排框架，确立了受控智能体执行的高层组件和责任边界（arXiv:2609.14824）。然而，该框架未说明如何将这些高层责任系统地分解为面向实施的低层架构。本文通过应用设计科学研究（DSR），将 ANASSA 框架从高层架构（HLA）责任细化为低层架构（LLA），以填补这一空白。所得设计在保留基础框架责任边界的同时，将其转化为可执行的架构结构，包括 47 个逻辑角色、版本化任务图、基于能力的智能体选择、类型化契约、显式运行时状态、空间验证机制、治理控制、故障与恢复路径以及溯源记录。该架构通过端到端执行轨迹进行演示，并从需求可追溯性、内部一致性、责任保留和技术独立性方面进行了分析评估。由此产生的从高层到低层的设计提供了 ANASSA 框架与未来系统实现之间面向实施的桥梁，定义了如何在将架构绑定到特定 AI 模型、地理空间平台、通信协议或部署环境之外，使受控的空间智能体工作流具备可检查性、可重构性和可测试性。

Agentic AI systems operating over geospatial data require more than autonomous reasoning and tool use; they must also support explicit orchestration, spatial validation, governance, provenance, failure handling, and human decision authority. Prior work introduced ANASSA, an agentic AI orchestration framework for spatial intelligence that established high-level components and responsibility boundaries for governed agentic execution (arXiv:2609.14824). However, the framework did not specify how those high-level responsibilities should be systematically decomposed into an implementation-facing low-level architecture. This paper addresses that gap by applying Design Science Research (DSR) to refine the ANASSA framework from high-level architectural (HLA) responsibilities into a low-level architecture (LLA). The resulting design preserves the responsibility boundaries of the foundational framework while translating them into executable architectural structures, including 47 logical roles, versioned task graphs, capability-based agent selection, typed contracts, explicit runtime states, spatial validation mechanisms, governance controls, failure and recovery paths, and provenance records. The architecture is demonstrated through an end-to-end execution trace and analytically evaluated for requirements traceability, internal consistency, responsibility preservation, and technology independence. The resulting high-level-to-low-level design provides an implementation-facing bridge between the ANASSA framework and future system implementations, defining how governed spatial agentic workflows can be made inspectable, reconstructable, and testable without binding the architecture to a specific AI model, geospatial platform, communication protocol, or deployment environment.