Public transportation agencies gather vast and heterogeneous datasets, yet their decisions still depend largely on manual queries and fragmented analyses. To address this gap, we introduce SUNTInsight, an agentic system that combines large language models, machine learning, and visual analytics to strengthen human decision-making. Through a seamless workflow, free-form prompts are automatically converted into SQL queries, relevant data are retrieved and processed, and an LLM, supported by visual interfaces, interprets the results to produce actionable findings. Applied to a case study in Salvador-Brazil, SUNTInsight revealed spatial heterogeneity, identified high-demand segments, and recommended targeted operational strategies, such as short turns and headway control, instead of broad fleet expansions. The implementation follows the principle of least privilege and runs generated code in isolated execution, mitigating risk while keeping humans in control.