Wastewater treatment plants (WWTPs) are highly complex systems where biological, chemical, and physical processes interact dynamically, creating significant operational challenges. Traditional modeling approaches, such as Activated Sludge Models (ASMs) and machine learning algorithms (MLAs), struggle to process the unstructured and multimodal data generated in WWTPs, limiting their effectiveness. Large Language Models (LLMs) offer a promising solution by integrating diverse data sources, recognizing patterns, and enabling human-in-the-loop interactions for informed decision-making. However, the complexity of WWTP operations exceeds the capabilities of a single LLM, necessitating a multi-agent framework where specialized agents collaborate to analyze diverse data streams and generate targeted recommendations. This perspective paper highlights how multi-agent, tool-equipped LLMs can enhance process control, optimize decision-making, and improve real-time adaptability in WWTPs. A case study on sludge bulking illustrates their potential over traditional methods. While challenges such as computational costs and AI-driven decision risks exist, they can be mitigated through validation, human oversight, and interpretability tools. Multi-agent LLMs represent a scalable and adaptable approach, positioning AI-driven decision support as a key innovation for WWTP operations.