Recent advancements in Large Language Models (LLMs) and Generative Artificial Intelligence (GenAI) have revolutionised software engineering (SE), augmenting practitioners across the SE lifecycle. In this paper, we focus on the application of GenAI within data analytics—considered a subdomain of SE—to address the growing need for reliable, user-friendly tools that bridge the gap between human expertise and automated analytical processes. In our work, we transform a conventional API-based analytics platform into a set of tools that can be used by AI agents and formulate a process to facilitate the communication between the data analyst, the agents and the platform. The result is a chat-based interface that allows analysts to query and execute analytical workflows using natural language, thereby reducing cognitive overhead and technical barriers. To validate our approach, we instantiated the proposed framework with open-source models and achieved a mean overall score increase of 7.2 % compared to other baselines. Complementary user-study data demonstrate that the chat-based analytics interface yielded superior task efficiency and higher user preference scores compared to the traditional form-based baseline.
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