The growing complexity of decision-making systems has led to the need for intelligent, adaptive, and transparent AI-driven solutions. This paper proposes SAFE multi-agent AI systems based on generative artificial intelligence which are instructed with target performance metrics that not only address accuracy but also robustness and explainability, thus making them Sustainable, Accurate, Fair and Explainable. Each agent operates as an employee who is responsible for making decisions, based on the comparison between performance metrics and given risk thresholds. Through our experiments and simulations, we demonstrate that SAFE agentic AI models can be effectively implemented.