An AI agent is a software system powered by a large language model (LLM) that can perceive its environment, make decisions, and take actions autonomously to complete a goal - often across multiple steps and without requiring human input at each stage.
The most important distinction between an AI agent and a standard chatbot lies in agency itself. A chatbot responds to a single prompt and returns a single response. An AI agent, by contrast, can plan a sequence of steps, call external tools, browse the web, write and execute code, query databases, or interact with third-party services - all in pursuit of a broader objective. Where a chatbot answers a question, an agent can act on the answer.
AI agents typically operate through a loop: they receive a goal, reason about how to achieve it, select and use a tool, observe the result, and then decide what to do next. This cycle repeats until the task is complete or the agent determines it cannot proceed. This pattern is often described as a reasoning and acting loop, sometimes referred to by the shorthand ReAct in the research literature.
Tools are central to how agents extend their capabilities beyond pure text generation. A tool might be a web search function, a code interpreter, a calendar API, or a file system interface. By combining Generative AI reasoning with real-world integrations, agents can handle tasks that would be impossible for a language model operating in isolation.
Many AI agent implementations also incorporate Retrieval-Augmented Generation (RAG) to give the agent access to up-to-date or domain-specific knowledge before generating a response or taking an action. This makes agents more accurate and grounded, particularly in professional or technical contexts.
In practice, AI agents are deployed across a wide range of use cases: automating research workflows, managing customer support queues, writing and deploying code, or orchestrating complex business processes. Some systems involve a single agent working alone, while others use multi-agent architectures, where several specialized agents collaborate, delegate tasks, and check each other's work.
The degree of autonomy an agent is granted varies considerably by application. Some agents operate with a human in the loop, pausing to request approval before taking consequential actions. Others run in fully automated pipelines with no human oversight at runtime. This spectrum of autonomy is one of the central design and safety considerations when building or deploying agent-based systems.
As the underlying models become more capable and tool ecosystems mature, AI agents are increasingly seen as a foundational building block for automating knowledge work at scale.