WebGIS development requires rigor, yet agentic AI frequently fails due to five large language model (LLM) limitations: context constraints, cross-session forgetting, stochasticity, instruction failure, and adaptation rigidity. We propose a dual-helix governance framework reframing these challenges as structural governance problems that model capacity alone cannot resolve. We implement the framework as a 3-track architecture (Knowledge, Behavior, Skills) that uses a knowledge graph substrate to stabilize execution by externalizing domain facts and enforcing executable protocols, complemented by a self-learning cycle for autonomous knowledge growth. Applying this to the FutureShorelines WebGIS tool, a governed agent refactored a 2,265-line monolithic codebase into modular ES6 components. Results demonstrated a 51\% reduction in cyclomatic complexity and a 7-point increase in maintainability index. A comparative experiment against a zero-shot LLM confirms that externalized governance, not just model capability, drives operational reliability in geospatial engineering. This approach is implemented in the open-source AgentLoom governance toolkit.

翻译：WebGIS开发需要严谨性，然而智能体AI经常因五大语言模型局限性而失效：上下文约束、跨会话遗忘、随机性、指令失效和适应僵化。我们提出一种双螺旋治理框架，将这些挑战重新定义为模型能力本身无法解决的结构性治理问题。我们将该框架实现为三轨架构（知识、行为、技能），利用知识图谱基底通过外化领域事实和执行可执行协议来稳定运行，并辅以自主知识增长的自学习循环。将此框架应用于FutureShorelines WebGIS工具后，受治理智能体将2,265行单体代码库重构为模块化ES6组件。结果显示圈复杂度降低51%，可维护性指数提升7分。与零样本LLM的对比实验证实，外化治理而不仅仅是模型能力，才是驱动地理空间工程操作可靠性的关键。该方法已在开源AgentLoom治理工具包中实现。