Abdullah Ali, Tamika Ramkissoon, Kevan Rajaram, Devon Murray, Letetia Addison, Kris Manohar, and Patrick Hosein · 12th Intelligent Systems Conference, Amsterdam, Netherlands

Small Island Developing States (SIDS) face acute capacity constraints, fragmented data ecosystems, and disproportionately high stakes for climate- and disaster-related decision making. Practitioners must rapidly synthesise information from heterogeneous sources—national plans, donor reports, sensor streams, and crowd-sourced signals—to design interventions, justify funding, and respond to fast-moving hazards such as floods. This paper investigates how agentic Retrieval-Augmented Generation (RAG)—a class of agentic systems that combine large language models with retrieval, tool use, and multi-step planning—can accelerate sustainable development workflows in SIDS contexts. We present a reference architecture that operationalises agentic RAG through a coordinator–specialist pattern and demonstrate it via an AI-driven Climate Resilience Platform for SIDS, a multi-agent conversational climate resilience system developed for flood risk assessment in Trinidad and Tobago. The AI-driven Climate Resilience Platform for SIDS integrates simulated weather sensors, river gauges, and social media sentiment with a domain knowledge base to produce traceable, location-specific risk evaluations through a natural-language interface. We evaluate the system on intent classification, risk assessment behaviour, and comparison with single-source and static baselines, achieving a macro-averaged F1 of 0.87 across seven intent categories. Results indicate that agentic RAG can support analysts and responders by lowering the technical barrier to multi-source decision support while preserving auditability through grounded outputs. We close with governance, evaluation, and deployment considerations specific to small-state institutional contexts.