An AI agent is usually a system that uses a model to choose and perform steps toward a goal. The useful distinction is whether it can act through tools and respond to results, not whether its interface looks like a chat box.
A simple working model
A person gives the system a goal, such as finding broken links on a site. The model proposes an action, a tool performs it, and the returned result informs the next action. A loop can continue until the system reaches a stopping condition or asks the person for input.
A chatbot that only writes an answer may be useful, but it is not doing the same job as a tool-using workflow. The boundary matters because taking actions introduces permissions, costs, and failure modes that a text response does not have.
Goal → choose action → call tool → inspect result → continue or stop
What makes one reliable
Memory can help with repeated tasks, but stale or incorrect memory can also mislead a system. A plan is useful only if execution checks reality after each step. For business workflows, start with a narrow, observable task such as sorting support requests before allowing the system to send messages or change records.
Give each tool the smallest permissions it needs.
Require confirmation before irreversible or external actions.
Record what actions were taken and what evidence supports the result.
Set time, cost, and step limits so a failed loop cannot run indefinitely.
Test with ambiguous and incomplete inputs, not only ideal examples.
Where to start
Choose a task with a clear success condition, safe tools, and a human review point. Measure whether the system completes the task accurately and how often a person must correct it. If an ordinary scripted workflow solves the problem reliably, an agent may add unnecessary complexity.
Sources and further reading
Primary documentation and references used to support this guide.