A Dual-Helix Governance Approach Towards Reliable Agentic Artificial Intelligence for WebGIS Development
arXiv:2603.04390v2 Announce Type: replace 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 cy
This research addresses fundamental limitations (context constraints, stochasticity) of current LLM-based agentic AI, which are increasingly seen as barriers to reliable real-world deployment.
The proposed dual-helix governance framework offers a practical solution to make agentic AI more robust and consistent, which is critical for its adoption in complex and sensitive applications.
The ability to reliably deploy agentic AI in environments like WebGIS refactoring signifies a potential jump in AI agents' practical utility, moving beyond proof-of-concept stages.
- · AI Agent Developers
- · WebGIS Development Teams
- · Software Refactoring Services
- · Knowledge Graph Providers
- · Legacy Manual Code Refactoring
- · Vendors of Inconsistent AI Solutions
- · Organizations Avoiding Agentic AI
- · LLM Providers Without Robust Governance Layers
Reliable agentic AI reduces the cost and time associated with complex software development and maintenance, particularly for legacy systems.
Increased trust and adoption of agentic AI in enterprise settings could accelerate the automation of white-collar tasks, impacting various industries beyond software development.
The success of externalizing knowledge via persistent graphs for agents might catalyze a broader architectural shift in AI systems, emphasizing explicit knowledge representation over purely black-box LLMs.
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Read at arXiv cs.AI