AI agents are entities that can act autonomously in an environment, taking information from their surroundings, making decisions based on data, and acting to transform circumstances. They can be seen in real-world applications such as robots, automated drones, and self-driving cars, or run inside computers to complete tasks. AI agents leverage large language models like GPT to understand goals, generate tasks, and complete them. They have different components that make up their body or software, including sensors, actuators, processors, control systems, and learning and knowledge base systems. Simple-reflex agents look for stimuli in one or a small collection of sensors, while model-based reflex agents gather information about how the world works and improve decision-making over time. Goal-based agents create strategies to solve problems, utility-based agents brainstorm outcomes of decisions, and learning agents learn from their surroundings and behavior. AI agents can be combined into multi-agent systems for complex tasks. They use sensors to gather data, control systems to think through hypotheses and solutions, actuators to carry out actions in the real world, and a learning system to keep track of progress and learn from mistakes. The development of AI agent platforms is rapidly advancing, with various apps available that can be used by developers and non-technical users alike. These apps include OpenAI Assistants API, AgentGPT, HyperWrite Assistant, aomni, Toliman AI, Spellpage, Do Anything Machine, LangChain, Pinecone, and Zapier Central. As AI agents become more prevalent, questions arise about their potential impact on society, including whether they will take our jobs, perpetuate bias and discrimination, and be held accountable for mistakes.