AI Agent

An AI agent is a system that autonomously plans and carries out multi-step tasks to reach a goal, rather than simply responding to a single request. Where a basic AI tool answers one question and stops, an agent can break a larger objective into steps, decide what to do next, use tools to act, observe the results, and adjust its approach until the goal is met. Give it an aim such as researching a topic and producing a summary report, and it can search, read, take notes, and write, chaining actions together with limited human intervention.

The mechanics revolve around a loop of reasoning and action. At the core sits a language model that interprets the goal and plans an approach. The agent then takes an action, which often means calling an external tool: running a web search, querying a database, sending an email, executing code, or updating a record. It reads the outcome of that action, incorporates what it learned, and decides on the next step, repeating the cycle until the task is complete or it determines it cannot proceed. This ability to use tools and to feed results back into its own planning is what separates an agent from a one-shot prompt. Many agents also keep a running memory of what they have done so they can stay coherent across a long sequence of steps.

The name pairs AI with agent, from the Latin agere, meaning to do or to act. The word captures the defining trait: an agent acts on the world toward a goal rather than only producing text. The concept gained traction as language models became reliable enough to drive these loops, and as developers connected them to tools that let the models actually perform work instead of merely describing it.

For a business, AI agents extend automation into territory that used to require human judgment at every step. A well-built agent can handle a workflow end to end, triaging incoming requests, gathering the information needed to resolve them, and taking the appropriate action, freeing staff for work that genuinely needs a person. The appeal is leverage: agents can operate continuously, handle many tasks in parallel, and coordinate across systems that would otherwise demand tedious manual glue. As the technology matures, they promise to shift AI from a tool people query to a worker that completes assignments.

A common mistake is granting an agent broad autonomy and access before it has proven reliable. Because an agent takes real actions, its errors compound: a wrong step early can send the whole task off course, and if it can send messages or change data, a mistake can have real consequences rather than just producing a bad paragraph. Agents also inherit the underlying model's tendency to hallucinate, so an agent can confidently pursue a flawed plan. The practical response is to constrain what an agent can do, keep a human in the loop for consequential actions, and test carefully within a narrow scope before widening it. AI agents connect closely to large language models that power their reasoning, to the prompts that direct them, and to the risk of hallucination that disciplined design must contain. Used thoughtfully, they are among the most powerful applications of current AI, and used carelessly, among the most error-prone.