Agentic AI for Climate-Resilient Building Retrofit: A Multi-Hazard Optimization Framework

Addressing building vulnerability to climate hazards requires advanced tools to support adaptation decisions. To this end, the current study presents an Agentic Artificial Intelligence (Agentic AI) Optimization framework to enhance the climate resilience of existing buildings, bridging policy guidelines and a practical tool for optimized and context-aware retrofit strategies. Aligned with EU Guidance, the framework operationalizes a Climate Vulnerability Assessment (CVA) within a Multi-Objective Optimization (MOO) engine through a multi-agent architecture. Specialized subagents, including Requirements, Cost, Strategy, and XAI Agents, collaborate to understand user goals, manage budget constraints, optimize strategies, and produce explainable reports. Two metaheuristic optimizers, such as Multi-Objective Invasive Weed (MO-IWO) and Grey Wolf (MO-GWO), were coupled with Multi-Criteria Decision Making (MCDM) models to minimize building vulnerability and adaptation costs against multiple climate hazards (e.g., heat waves and heavy precipitation). Results show that, despite MO-GWO’s lower computational burden, MO-IWO performed more robustly and is selected as the superior optimizer for integration into the Agentic AI system. Ultimately, the framework provides a scalable approach to asset management, significantly improving decision-making for building retrofits.