The widespread dissemination of hate speech across digital platforms has encouraged the development of LLM-based counter-narrative defenses. Existing approaches face critical limitations in adaptability, evidence integration, and transparency. While the use of human-generated datasets and fine-tuning approaches has paved the way in recent studies, these methods struggle with evolving hate patterns, require costly retraining, and often produce generic responses that lack the evidence-based reasoning necessary to effectively counter sophisticated, harmful arguments. Moreover, exposing human annotators to hate speech during dataset creation raises significant ethical concerns and introduces systematic biases. This thesis presents a novel adversarial debate framework in which LLM agents assume opposing roles, with one defending problematic positions and the other developing evidence-based counterarguments through structured eight-turn debates.