AI agents are systems where a language model doesn't just generate text - it decides what actions to take, executes them, observes the results and adapts its approach accordingly. Give an agent access to a web browser, a calculator and a code interpreter, and it can research a topic, perform calculations, write and run code to analyse data, before synthesising everything into a report - all from a single request. The model acts as the "brain" that plans and reasons, while the tools provide capabilities the model doesn't inherently have: accessing current information, performing precise calculations, interacting with APIs or manipulating files. This is a significant leap from simple chatbots. An agent can handle open-ended tasks that require multiple steps and adaptation based on intermediate results. But agents also introduce new risks: a model that can take actions in the real world can take wrong actions - for example, sending incorrect emails, making bad API calls or getting stuck in loops. The reliability requirements are much higher than for simple text generation, because mistakes have real consequences. For businesses, agents represent the next frontier of AI value, but they demand careful design, robust error handling and appropriate human oversight, especially for high-stakes actions.