The discovery of new molecules with specific properties represents a major challenge in modern chemistry and drug development. Traditional methods involve testing thousands or even millions of different molecules, which is time-consuming and costly. This thesis presents AutoSMILES, an innovative framework based on a multi-agent architecture for AI-driven molecular design. The core innovation lies in employing two agents: a generative agent that creates molecules and a prompt agent that provides dynamic, contextual feedback based on historical generation attempts. This agentic approach o!ers paths of solutions to solves fundamental problems of iterative generation and inefficiency that limit traditional systems. The research reveals crucial discoveries in prompt engineering for computational chemistry.