Artificial intelligence (AI), the science and engineering of artificial intelligent agents, is a multidisciplinary and ubiquitous field that has become increasingly important to society. In fact, some argue that AI is at the heart of a new industrial revolution. However, with the rapid pace of research and development, the impact of AI on our lives is still unfolding. While there are certainly a myriad of positive impacts, there are also concerns that have led many experts to call for greater regulation and control. In this chapter, we address the main paradigms of AI and specifically focus on the perspective of artificial intelligent agents, a common denominator for all the paradigms and one of the most consensually accepted approaches for AI, without forgetting their interaction with human agents. We start by reviewing the history of AI and contextualizing its major paradigms over time, highlighting the key breakthroughs that have brought AI to its present state of grace. From there, we delve into the perspective of artificial intelligent agents and the various technologies that rely on them. We superficially examine issues related to coordination, cooperation, and negotiation between artificial and human agents, with special focus on human-AI agents cooperation. We emphasize the importance of viewing these agents as part of a broader ecosystem in which both humans and artificial agents “live” in a symbiotic relation. In the light of this ecosystem, where human-AI cooperation flourishes, we examine the human and machine in the loop concepts, more precisely, we discuss the various ways in which humans are involved in AI, including as sources of data for AI agents, beneficiaries of AI agents output, and in the design of AI agents (e.g., creation of knowledge representation structures, and coding decision-making and machine learning algorithms). We overview the fundamental areas of machine learning, including reinforcement learning and the related fields of learning by demonstration, apprenticeship learning, imitation learning, and inverse reinforcement learning, which hold great promise for the future of AI. Finally, we discuss the limitations of AI, the challenges AI faces, the opportunities that lie ahead, with all their positive and negative impacts.