At its core, an AI agent is a software program powered by artificial intelligence that can perceive its environment, make decisions, and take actions to achieve a goal—often autonomously. Unlike traditional software that follows rigid, pre-programmed instructions, AI agents can operate with varying degrees of autonomy, learning from their interactions and adapting their behavior accordingly. An AI agent's perception-think-action loop involves gathering context, reasoning through what's being asked or perceived, planning steps to achieve a goal, executing actions using tools, and learning from outcomes. The key difference between an AI agent and other AI systems like LLMs, chatbots, and workflows is their autonomy, tool use capabilities, and proactive approach to achieving goals. With the ability to leverage external tools, agents can extend their capabilities beyond what's built into their core model. Modern AI agents are complex systems comprised of several critical components working together to create intelligent behavior, including foundation AI models, memory systems, tool use systems, planning and reasoning systems, agent frameworks and orchestration, knowledge retrieval mechanisms, and security and safety systems. These components work together to enable the agent's perception-think-action loop, allowing it to perceive its environment, make decisions, and take actions to achieve a goal. The development of AI agents is an active area of research, with various applications across industries such as software development, business operations, healthcare, education, personal productivity, and more. However, challenges arise, including alignment problems when agents go off-track, the black box problem where it's unclear why the agent made a particular decision, security headaches due to new attack surfaces, and the responsibility question of who is accountable for an agent's actions. As AI agents continue to evolve, it's essential to address these challenges and consider the human-agent relationship carefully to ensure that agents enhance human capabilities rather than replace them.