"AGENTIC AI–DRIVEN DECISION-SUPPORT FRAMEWORK FOR CLIMATE-RESPONSIVE AGRICULTURAL ADAPTATION USING REINFORCEMENT LEARNING”

Climate variability is very sensitive to agriculture that interferes with crop productivity, resources management and sustainability. Conventional decision support systems (DSS) have brought meaningful understandings but this is constrained by its static and rule-based structures that cannot hold up well in a dynamic and uncertain climatic environment. To help fill that gap, this paper offers an agentic AI-based decision-support approach to climate adaptation in agriculture that combines reinforcement learning (RL), digital twins, and multi-agent reasoning. The framework was also derived and validated with regard to climate, soil, and crop datasets and tested by a digital twin simulation environment. Agents at the reinforcement learning were trained under Deep Q-Learning, Proximal Policy Optimization, and Actor Critic algorithms and Proximal Policy Optimization proved to converge faster and illustrated relative stability. The results indicate drastic increases in the yield of crops under normal and stressful conditions, improved water-use efficiency, and optimized fertilizer use and by increasing use of biochar and recycling water, the carbon footprint can be reduced relative to the usual DSS. In addition, the Agentic AI layer provided pipeline flexibility with respect to changes in policy like water-use limitations, resulting in a reduced recalibration time and increase in the adaptability index. The comparative analysis, against the conventional models of DSS, assures that the proposed framework can both balance productivity and sustainability and continue being robust in a wide range of climatic conditions. The significance of observed improvements was proved by statistical validation with ANOVA and paired t-tests (p < 0.05). These results imply that reinforcement learning and digital twin integration with Agentic AI offers a high capacity and robust channel towards future climate change-resistant agriculture decision-making.