A Robust, Domain-Aware Agentic System for Interactive Web-Based Aquaculture Simulation

As aquaculture systems become increasingly complex, simulation tools must evolve to support intuitive, reliable, and domain-specific exploration. In our earlier work, we developed a web-based platform that combined Functional Mock-up Units (FMUs), surrogate modeling techniques, and a basic chatbot assistant to simulate key components of aquaculture systems such as fish growth, water quality, and behavior. While this platform improved simulation efficiency and usability, it lacked the contextual awareness and flexibility needed to fully support users in configuring and understanding complex simulation scenarios. In this paper, we present an enhanced, robust agentic system that builds on the previous platform by introducing a more intelligent, domain-aware AI assistant. This assistant is powered by a knowledge-augmented generative architecture (KAG-RAG), which integrates a Neo4j-based knowledge graph constructed from aquaculture-specific tutorials and documentation with a Large Language Model (LLM). The result is an assistant capable of interpreting natural language queries, retrieving and reasoning over relevant context, and guiding users through simulation setup and analysis in real time. To improve the overall user experience, the system provides more context-aware assistance to help users configure simulations correctly and avoid common setup errors. We compare our enhanced agent with two baselines: one using only the OpenAI API and another using a standard RAG setup. Our findings show that the agent delivers more accurate, relevant, and context-aware responses. It better understands complex, multi-step queries, uses aquaculture terminology more effectively, and provides clearer, more helpful guidance during simulation. Overall, this agentic system offers a more powerful, adaptive, and user-friendly environment for researchers, policymakers, and industry professionals, supporting the development of sustainable, efficient, and economically viable aquaculture practices.