A dual-helix governance approach has been proposed to address the limitations of agentic AI in WebGIS development, which is hindered by five key challenges: context constraints, cross-session forgetting, stochasticity, instruction failure, and adaptation rigidity. This framework views these limitations as structural governance problems that cannot be resolved by model capacity alone1. By reframing these challenges, the approach aims to provide a more reliable and robust foundation for agentic AI in WebGIS development. The proposed framework has significant implications for the development of more advanced and autonomous AI systems, particularly in applications where reliability and consistency are critical. As state-aligned threat activity raises the stakes from criminal to geopolitical, the development of more robust AI governance frameworks is crucial. This matters to practitioners because it highlights the need for a more comprehensive approach to AI development, one that prioritizes governance and reliability alongside model capacity, to mitigate potential risks and threats.