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

arXiv:2603.04390 · cs.AI, cs.SE · Submitted 2026-03-04 · Read on arXiv

cs.AI, cs.SE

Submitted: 2026-03-04

Updated: 2026-07-02

Comments: Paper submitted to and under review in Transactions in GIS

Journal ref: Transactions in GIS (2026). 30, no. 6: e70402

License: http://creativecommons.org/licenses/by-sa/4.0/

The gist: WebGIS development requires consistency, yet agentic AI often fails due to LLM context constraints, forgetting, stochasticity, instruction failure, and adaptation rigidity.

Terminology

Knowledge Graph — 25× in this paper · explained in 7 episodes
Ablation — 20× in this paper · explained in 1 episode
Adaptation — 12× in this paper · explained in 1 episode
Agentic AI — 12× in this paper · explained in 1 episode
Consistency — 11× in this paper · explained in 3 episodes
Core — 10× in this paper · explained in 2 episodes
Aggregation — 7× in this paper · explained in 1 episode
Ablation study — 6× in this paper · explained in 1 episode
Accuracy — 6× in this paper · explained in 1 episode
Knowledge graphs — 6× in this paper · explained in 2 episodes
Prompt Engineering — 6× in this paper · explained in 4 episodes
RAG — 6× in this paper · explained in 3 episodes
Agentic System — 5× in this paper · explained in 2 episodes
Fine-Tuning — 5× in this paper · explained in 5 episodes
Generalizability — 5× in this paper · explained in 6 episodes
Hallucination — 5× in this paper · explained in 3 episodes
Foundation Model — 4× in this paper · explained in 3 episodes
Modular Architecture — 4× in this paper · explained in 4 episodes
Provenance — 4× in this paper · explained in 3 episodes
Agentic Reasoning — 3× in this paper · explained in 1 episode
Agentic Workflows — 3× in this paper · explained in 1 episode
Attention Mechanism — 3× in this paper · explained in 2 episodes
Benchmark — 3× in this paper · explained in 2 episodes
Grounding — 3× in this paper · explained in 3 episodes
Human-in-the-Loop — 3× in this paper · explained in 3 episodes
Indian River Lagoon — 3× in this paper
Retrieval-Augmented Generation — 3× in this paper
Data Provenance — 2× in this paper · explained in 2 episodes
Failure Modes — 2× in this paper · explained in 3 episodes
Foundation Models — 2× in this paper · explained in 7 episodes
Geographic Information Systems — 2× in this paper
LLM-as-a-Judge — 2× in this paper · explained in 3 episodes
Retrieval-Augmented Generation (RAG) — 2× in this paper · explained in 16 episodes
Source Lines of Code — 2× in this paper
Verification — 2× in this paper · explained in 2 episodes
Agentic AI systems — 1× in this paper · explained in 1 episode
Agentic Approaches — 1× in this paper · explained in 1 episode
Agentic Artificial Intelligence — 1× in this paper · explained in 1 episode
Alignment — 1× in this paper · explained in 1 episode
Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
Chain-of-Thought (CoT) Prompting — 1× in this paper · explained in 3 episodes
Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
Generative AI — 1× in this paper · explained in 2 episodes
Hybrid Approach — 1× in this paper · explained in 4 episodes
Integrated Development Environment — 1× in this paper
Interpretability — 1× in this paper · explained in 15 episodes
Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
Modularity — 1× in this paper · explained in 9 episodes
Multimodal Fusion — 1× in this paper · explained in 3 episodes
Ontology — 1× in this paper · explained in 2 episodes
Predictive Modeling — 1× in this paper · explained in 2 episodes
Redundancy — 1× in this paper · explained in 2 episodes
Robustness — 1× in this paper · explained in 5 episodes
Scaffolding — 1× in this paper · explained in 3 episodes

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.

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

Terminology

Abstract

Sources

Related papers