Conversational agents (CAs), such as chatbots or virtual assistants, represent Artificial Intelligence (AI) systems designed to facilitate human–machine interaction through natural language. These systems are revolutionizing communication by improving efficiency, effectiveness, and user-friendliness. CAs find applications across various domains, including customer service, health care, and education. This chapter thoroughly explores CAs, focusing on their underlying structure, the problems they aim to address, and the current challenges documented in the existing literature. Furthermore, we delve into the architectures of CAs, shedding light on the key distinctions between modular and end-to-end implementations. To achieve this objective, we introduce a comprehensive taxonomy categorizing the tasks CAs are designed to tackle, encompassing information retrieval, question answering, and chitchat. We scrutinize the advantages and disadvantages of potential models for each of these tasks, besides comprehensively investigating the recent developed methodologies and incorporating a detailed analysis of rule-based and data-driven strategies. Throughout this examination, we emphasize the strengths, limitations, and potential future directions, including the imperative need to develop ethical and reliable conversational agents.