AI agents are autonomous systems capable of decision-making and task execution without human intervention, utilizing tools such as memory and knowledge graphs to contextualize and reason through diverse scenarios. These agents are applied across various sectors, including healthcare, finance, and manufacturing, where they perform tasks ranging from customer service to complex automation processes. Despite their transformative potential, the adoption of AI agents faces challenges like high implementation costs, data privacy issues, and ethical concerns, which contribute to user mistrust. Continuous advancements in AI research aim to address these challenges by reducing model biases and improving accuracy, while declining hardware costs may enhance accessibility and foster new applications. AI agents are characterized by their ability to perceive their environment, make informed decisions, and act on them, often employing multiple models and tools to achieve their objectives. They differ from conventional AI systems by being less reliant on user input and more capable of adapting to dynamic environments through real-time data utilization and feedback loops. The integration of advanced components such as graph databases and sophisticated memory systems allows AI agents to manage complex relationships and retain past interactions, enhancing their decision-making abilities and personalizing user interactions over time.