The Cherenkov Telescope Array Observatory (CTAO) represents the next generation in ground-based gamma astronomy, marked by a substantial increase in complexity with dozens of telescopes. This leap in scale introduces significant challenges in managing system operations and offline data analysis. Traditional methods, which depend on advanced personnel training and sophisticated software, become increasingly strained as the system's complexity grows, making it more challenging to effectively support users in such a multifaceted environment.

To address these challenges, we propose the development of AI agents based on instruction-finetuned large language models (LLMs). These agents align with specific documentation and codebases, understand the environmental context, operate with external APIs, and communicate with humans in natural language. Leveraging the advanced capabilities of modern LLMs, which can process and retain vast amounts of information, these AI agents offer a transformative approach to system management and data analysis by automating complex tasks and providing intelligent assistance.

We present prototypes aimed at integrating with CTAO pipelines for operation and offline data analysis. The first prototype is a plugin that implements chatting and function calling for the Configuration Database of Array Configuration and Data Acquisition (ACADA). This plugin enhances operational efficiency by providing intuitive, context-aware assistance and automating routine tasks. The second prototype is an open-access custom ChatGPT tailored for the gammapy-based data analysis, which offers interactive support for researchers.