Agentic Artificial Intelligence for Subnational SDG Localisation: a Human-in-the-Loop Framework for Voluntary Local Reviews in Sarawak, Malaysia
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Abstract
Voluntary Local Reviews (VLRs) have become the main instrument through which subnational governments report their contribution to the Sustainable Development Goals (SDGs), yet most remain qualitative because of local data gaps, uneven indicator coverage and limited staff capacity. At the same time, artificial intelligence has advanced from single-task models to agentic systems that plan, retrieve evidence and use tools across multi-step workflows. This article asks how agentic AI can support the full VLR cycle in a data-constrained, multilingual region of the Global South without displacing local ownership and accountability. Using a design science research approach, it derives six design requirements from the literature and develops the Agentic Voluntary Local Review (A-VLR) framework: a four-layer architecture in which an orchestrator coordinates specialised agents for policy–SDG mapping, indicator localisation, data harvesting and estimation, and evidence-based drafting, under mandatory human-in-the-loop checkpoints and a cross-cutting assurance agent. The framework embeds evidence traceability, multilingual community input, Indigenous data governance and sovereign deployment in its design. Its application is illustrated through Sarawak, Malaysia, where an SDG-aligned state strategy, a growing number of councils preparing VLRs and state investment in AI coincide. The article introduces the concept of computational localisation, proposes an evaluation protocol and offers four testable propositions. It argues that frontier AI can help local governments move towards traceable, inclusive and evidence-based SDG reporting, provided that humans and communities remain in charge of what is reported
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