How it works
An AI agent combines a model, instructions and controlled access to resources. These resources can be tools, such as search, a terminal, an API or a knowledge base. The model interprets the request and decides which step makes sense within the available permissions.
The cycle usually includes observing the context, choosing an action, executing the action and evaluating the result. If a tool response shows that something failed, the agent can try another path. A well designed system also defines when to stop and when to ask for human confirmation.
Not every product called an agent has the same degree of autonomy. Some follow fixed steps written in code. Others decide dynamically which tools to use. Therefore, when evaluating an agent, check what it can access, which actions it can execute and how it logs what it did.
Practical example
Imagine a programming agent tasked with fixing a failing test. It can read the task description, locate the relevant files, propose a change and run the available tests. The test results provide new information for the next step.
This flow does not mean the change is correct or safe. A person still needs to review the diff, check whether the change meets the requirement and assess effects that tests do not cover. If the agent has write access, the environment should limit the files and commands it can modify.
In a small store, another agent could classify incoming messages and prepare draft replies. A prudent configuration leaves final sending for human approval, especially when the message involves money, personal data or a promise to the customer.
What it's for
Agents are useful when a task requires several steps and can benefit from tools, context and result verification. Examples include research with sources, ticket triage, report preparation and development tasks accompanied by tests.
For a simple question, a direct call to a model can be easier to control. An agent adds decisions, tools and intermediate states, which also creates more points of failure. Start with the smallest automation that solves the problem and increase autonomy only when there is an observable benefit.
Before putting an agent into production, define minimum permissions, execution limits, logs and a way to interrupt it. Test first on data and environments that do not cause real impact. Measure the quality of the complete task, not just whether the agent produced a convincing response.