Existing LLM-based approaches remain limited by simplistic task execution, restricted tool integration, and a lack of contextual reasoning when interacting with professional GIS software. This study investigates the efficacy of a multi-agent architecture designed to enhance geospatial task execution accuracy through collaboration, reasoning and tool-use. The architecture integrates Chain of Thought (CoT) reasoning and Retrieval-Augmented Generation (RAG) and employs specialized agents that collaboratively translate high-level natural language queries into structured, executable workflows using QGIS processing algorithms as tools. Through a structured fine-tuning approach, we evaluated how the multi-agent setup influences spatial task comprehension, geoprocessing tool selection, and code generation. The results demonstrate that the developed framework significantly outperforms baseline single-agent and non-fine-tuned systems. For tasks involving one or two GIS tools, the system achieved up to 100% execution success and 87.5% semantic correctness. However, its effectiveness decreases with more complex, multi-step workflows. Notably, iterative self-refinement and self-debugging led to moderate gains in execution success and semantic correctness. The results indicate that multi-agent frameworks are a promising approach, even though improvements in reasoning depth and tool alignment are still needed. This study represents a foundational step toward more reliable, modular, and adaptable LLM-based systems for geospatial analysis automation.
Mansourian et al. (2026) studied this question.