An LLM-based software developer agent demonstrated through a port-logistic optimization case study

Advancements in artificial intelligence (AI) are having a significant impact across various domains. Among these, generative AI models – particularly large language models (LLMs) – have brought substantial changes to a wide range of activities and professions. As these models are capable of handling complex business workflows and interacting with external tools (e.g., via APIs), their agentic behaviour becomes especially valuable for supporting diverse workflows. Software development is one of the domains that can greatly benefit from the use of LLM-based agents. We have developed a software developer agent using the LangGraph platform, capable of generating and validating the software requirements specification (SRS), design document specification (DDS), project structure, function implementations, and corresponding unit tests – all based on a well-formulated input prompt. After successfully testing the agent on the relatively simple task of generating a Snake game, we applied it to a more complex problem: an optimization scenario. A simulation framework was developed to optimize port logistics processes, such as routing and scheduling trucks and vessels. This framework allows for port structure customization and, thanks to its modular design, supports the evaluation of various optimization algorithms. Using this framework, we investigated how our software developer agent performs on a significantly more complex coding task compared to the Snake game. We also compared the results with those generated by two widely used LLM-based systems: ChatGPT and Gemini Code Assist.