What an AI agent is, and what it is not

An AI agent is software that can choose steps toward a goal and use connected tools to carry them out. For example, you might ask it to review new inquiries and prepare draft replies. It uses an AI language model, the part that interprets instructions and generates responses, to decide what to do next. You still set the task, access and approval rules.

What is the difference between a chatbot, an automation, and an agent?

These categories overlap. A chatbot is a conversational interface, and some chatbots can also use tools as agents. A fixed automation follows rules you set, such as saving an attachment when an email arrives. An agent can choose among steps based on what it finds. Ask a provider to show those decisions in a real example.

Follow an inquiry through a simple example

A customer sends a message asking whether your business can help with an upcoming event. A fixed automation can copy that message into your customer list and notify a colleague. It follows the same rule each time.

An agent could take the next step by reading the message and comparing it with your approved service information. If the date is missing, it might prepare a question. If the request concerns a service you do not provide, it might draft an appropriate reply. The next step depends on what it finds.

Your team would then check the proposed response before sending it. The potential value is having the relevant information gathered and a draft ready. Whether that saves time depends on how accurate the draft is and how much review it needs.

What does the surrounding software do?

A model is the part of the system that interprets instructions and proposes what to do. It needs other software to open files, use applications and save the result. Products such as Claude Code and Codex provide parts of that working environment.

Think of asking for a weekly report. The model may know how to write one, but it still needs access to the correct source files and a way to create the document. The surrounding software supplies those connections. It also needs to keep track of what has happened so the task can continue or stop at the right point.

Useful tasks to try first

• Customer inquiries: sort messages and prepare replies for your team to check.
• Research: gather sources and draft a summary with links you can verify.
• Writing: prepare a first draft of a follow-up, report or article.
• Reporting: assemble recurring figures and flag missing information.

An agent is most useful when you can explain the goal but the steps vary. If every incoming attachment always belongs in the same folder, a fixed rule may be enough. If messages need to be read and handled differently, AI may help with that judgment. You can combine the two approaches in one process.

Where does the human stay in the loop?

Keep a person responsible for actions that spend money, publish content, delete records or make promises to customers. The agent can prepare the work and explain its proposed next step. An approval gate is the point where it stops and asks a person to review that action. Ask a vendor to demonstrate both approval and rejection.

An approval should show the actual action: the recipient and message before an email is sent, or the exact record before it is changed. A vague “continue?” prompt gives the reviewer too little information. The person needs enough context to approve, correct or reject the action.

In this test, the newer model completed more tasks, but it still fell well short of completing all of them. That does not predict its success rate in your business. Try one clearly defined job and check every result before deciding how much responsibility to give it.

A benchmark is a defined set of test tasks. In the chart, a higher percentage means more of those tasks were completed under the test conditions. Your business has different files, instructions and exceptions, so you need a small trial using your own examples before relying on similar performance.

Set a spending limit and a stopping rule before a task runs on its own. For example, an unsuccessful attempt to read a file should end with a request for help after an agreed limit, rather than repeatedly trying and adding charges. Keep a record of the steps so your team can understand what happened.

How do you start without betting the business?

Begin with a task your team already understands and can check easily. Write down what a correct result contains, supply a few representative examples and review the drafts. Count the time spent correcting them. If the task works well enough to continue, document how your team runs it and handles exceptions.