TOWARDS AUTOMATED BUILDING MAINTENANCE AND RENOVATION: A MULTI-AGENT LLM-BASED APPROACH

Abstract: Building maintenance and renovation decisions rely heavily on expert judgment and contextual factors, often requiring manual assessment of conditions, accumulated experience, and locally applicable standards or regulations. This study introduces a multi-agent AI framework designed to automate recommendations for building maintenance and renovation by coordinating task classification, regulatory analysis, and solution generation through LLMs. Specifically, given inputs including a building’s location, type, and reported issues, the framework first uses a task identification agent to classify the case as maintenance or renovation. A knowledge agent then retrieves, structures, and reasons over applicable building codes, standards, and maintenance guidelines specific to the issue. Building on this contextualized knowledge, a solution generation agent produces maintenance recommendations or renovation strategies, or evaluates proposed renovation plans for regulatory compliance. The proposed multi-agent architecture enables the automation of key reasoning steps that are traditionally performed by human experts, including task classification, regulation interpretation, and recommendation formulation.