An AI agent is software that can choose and carry out steps toward a goal. It may search a knowledge base, read a file, call an application programming interface, run code, or update a record. The common short name for an application programming interface is API.
A normal chatbot often returns text and stops. An agent can use its answer as a plan for another action. It sees the result, adjusts the plan, and continues until it reaches a stopping point.
A goal turns into a series of actions
Take a travel research agent. The user gives dates, a budget, and a destination. The agent searches approved sources, compares options, and builds an itinerary. It may notice that a museum closes on Tuesday and move that visit. Each tool result changes the next choice.
The language model does not contact a booking system through thought alone. The surrounding software gives it a list of tools with clear input rules. One tool may search flights. Another may read calendar events. The application runs the chosen tool and returns the result to the model.
The loop needs a firm boundary
An agent usually follows a loop: inspect the current state, select an action, observe the result, and decide whether more work remains. The software should also set a maximum number of steps, time limit, and spending limit. Without those controls, a mistaken plan can repeat calls or consume resources.
Memory can help across a long task. Short-term memory stores the active plan and tool results. Long-term memory may save selected facts for later sessions. Saved notes can become outdated or wrong. Good systems track the source and date of important information.
Authority matters more than fluency
A confident explanation causes limited harm when it stays in a draft. An agent with permission to send email or change a database can affect other people. Each tool should receive only the access needed for its job. A research task does not need payment permission.
High-impact actions need confirmation. The system can prepare an order and ask the user before purchase. It can draft a message without sending it. Approval points keep a person in control while the agent handles routine work.
How an agent can fail
The model may choose the wrong tool, invent a parameter, misunderstand a result, or stop too early. A malicious web page can hide instructions aimed at the agent. This attack is called prompt injection. Tool output should count as untrusted data rather than higher-priority guidance.
Tests need full tasks rather than polished sample chats. Teams check whether the agent reaches the right outcome, uses allowed tools, respects limits, and recovers from errors. Logs should record actions and results without exposing secrets.
An agent does not need unlimited freedom to be useful. A narrow agent that updates delivery addresses under clear rules may work better than one asked to “manage customer service.” Specific goals, small toolsets, and visible approval points make behavior easier to judge.