Can the Production of Territorial and Environmental Diagnosis by Consulting Firms Be Co-Piloted? An Experimental Analysis of the Potential Offered by Multi-Agent Frameworks

Recent advances in Generative AI are creating new opportunities for engineering and geosciences activities. This study examined whether the initial production of Territorial and Environmental Diagnosis (TED) reports could be co-piloted by multi-agent systems. The study proposed a challenge between a classical engineering workflow and a multi-agent framework (MAF) combining specialized agents boosted by LLMs for data retrieval, Data Visualization, writing, statistical description, and publishing within a unified deterministic workflow. To evaluate the effectiveness of the MAF, a blind comparative assessment was conducted using sections of TED reports produced by internal experts for three French municipalities as reference case studies. The framework was tested across several key thematic sections, and MAF outputs were compared with the outputs generated by territorial experts. The evaluation focused on Data Relevance, Writing Quality, Data Visualization, Contextual Relevance, and Overall Rating. The results of our experiments indicate that MAF reduced, within tested conditions, the time required for reporting while maintaining a level of quality comparable to conventional reports. Evaluations confirmed that MAF performed well in themes relying on processing data, while human-produced reports retained an advantage in topics requiring deeper contextual interpretation. These findings support the potential of MAF as co-pilots for TED production in engineering workflows.