An AI agent is like a capable personal assistant who doesn’t just answer questions, but can actually carry out tasks on your behalf. While a language model is like a knowledgeable librarian who can tell you about information, an agent is like an assistant who can take that knowledge and use it to accomplish real-world goals.
Imagine an assistant who, when you ask them to process a vendor invoice, doesn’t just extract the data—they can actually verify the purchase order matches, check if the vendor is approved, flag any discrepancies, update your accounting system, and route it to the right person for approval. This assistant can access multiple databases, make decisions based on company policies, and escalate issues if something looks unusual.
Similarly, an AI agent is built on top of a language model but enhanced with the ability to take actions and use tools. The agent can break down complex tasks into steps, interact with different systems (like databases, APIs, or software), observe the results of its actions, and decide what to do next.
Just as your personal assistant might send emails or update spreadsheets to complete a project, an AI agent can execute code, search databases, retrieve web information, or interact with other software to accomplish its goals. The agent operates somewhat autonomously, making decisions about which tools to use and when.
However, agents still have limitations. They work best with clear goals and appropriate guardrails, and should be supervised, especially when performing consequential actions. Like an assistant who might occasionally misunderstand instructions, agents can sometimes take unexpected actions or struggle with tasks requiring nuanced judgment.