Since the emergence of the LLM trend, initiated with the first release of ChatGPT [1], these systems have undergone continuous development and have evolved into multimodal architectures. Multimodal models, such as GPT-4o [2], LLaMA 3.2 [3] and Claude with its latest 3.5 Sonnet model [4], integrate language understanding with non-language capabilities, including vision and audio processing. This progression unlocks new opportunities for developing intelligent agents capable of recognizing and interpreting patterns not only at a semantic level but also through components that can incorporate other types of unstructured data into prompts, significantly expanding their potential applications and impact. Extending these capabilities, Vision-Language Models (VLMs) build on multimodal principles by integrating visual reasoning into the LLM framework. By introducing new tokens into the prompts to process image frames, VLMs enable simultaneous semantic and visual reasoning. This enhancement is particularly valuable in dynamic applications like robotics, where the integration of vision and language reasoning enables systems to generate environment-responsive actions. Such actions, often described as descriptive policies, translate reasoning into meaningful, executable commands. Language models able to generate such commands are usually referred to as "agentic".

Recent trends are emerging in the use of Large Language Models (LLMs) as autonomous agents that take actions based on the content of the user text prompts. We intend to apply these concepts to the field of Guidance, Navigation, and Control in space, enabling LLMs to have a significant role in the decision-making process for autonomous satellite operations. As a first step towards this goal, we have developed a pure LLM-based solution for the Kerbal Space Program Differential Games (KSPDG) challenge, a public software design competition where participants create autonomous agents for maneuvering satellites involved in non-cooperative space operations, running on the KSP game engine. Our approach leverages prompt engineering, few-shot prompting, and fine-tuning techniques to create an effective LLM-based agent that ranked 2nd in the competition.