A four-dimension classifier routes agentic coding tasks into HITL, human-over-the-loop, or automated-with-monitoring tiers, analytically estimated to keep ~91% of ungoverned coding velocity under regulatory constraints.
A Dual-Helix Governance Approach Towards Reliable Agentic Artificial Intelligence for WebGIS Development
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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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cs.HC 1years
2026 1verdicts
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Governed AI-Assisted Engineering: Graduated Human Oversight for Agentic Code Generation in Regulated Domains
A four-dimension classifier routes agentic coding tasks into HITL, human-over-the-loop, or automated-with-monitoring tiers, analytically estimated to keep ~91% of ungoverned coding velocity under regulatory constraints.