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arXiv · 2603.04390

A Dual-Helix Governance Approach Towards Reliable Agentic Artificial Intelligence for WebGIS Development

Boyuan Guan·Wencong Cui·Levente Juhasz
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Abstract

WebGIS development requires consistency, yet agentic AI often fails due to LLM context constraints, forgetting, stochasticity, instruction failure, and adaptation rigidity. We propose a dual-helix governance framework reframing these as structural problems rather than capacity deficits. Using a 3-track architecture (Knowledge, Behavior, Skills) and a persistent knowledge graph, it stabilizes execution by externalizing facts and enforcing protocols. Validation shows a governed agent successfully refactored a legacy WebGIS codebase (reducing cyclomatic complexity and improving maintainability), roughly halved trial-to-trial output variance relative to static prompting in a controlled experiment, and prevented common infodemic mapping errors in a 5-condition COVID-19 cartography ablation study. Operationalized via the open-source AgentLoom toolkit, this externalized governance provides the stability necessary for production-level geospatial engineering.

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Boyuan Guan, Wencong Cui, Levente Juhasz. 2026-03-04. A Dual-Helix Governance Approach Towards Reliable Agentic Artificial Intelligence for WebGIS Development. https://arxiv.org/abs/2603.04390

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