The short answer
An AI agent is a language model that receives a goal, decides which steps to take and uses tools to reach it: it reads an email, checks a spreadsheet, looks up an order, prepares a record. It does not just answer questions. It acts, within the limits the company sets for it.
For a business, this is useful in tasks with several steps and inputs without a fixed format, such as checking documents against records. The care lies in the limits: what the agent can read, what it can do on its own and at which point a person needs to approve before the action happens.
What an AI agent is, in practice
Underneath every agent there is a language model, the same kind of technology behind chat assistants. The difference is in what surrounds it. The agent receives instructions about the work, a list of tools it can call and rules it cannot break. At each step, the model looks at what it already knows, chooses the next action, sees the result and decides what to do next, until it finishes the task or stops at an approval point.
Tools are what give the agent its reach: reading an email inbox, opening a file in Drive, checking a spreadsheet, calling the API of an internal system. Without tools, the model only writes text. With them, it can carry out a routine from start to finish.
This set of instructions, tools and rules around the model has a technical name, harness. That is where safety lives: the agent only reaches what has been connected to the environment and only does what the rules allow. On the automation and artificial intelligence page we show an example of this environment at work.
What is the difference between an AI agent, a chatbot and automation
The three terms get mixed up, but they solve different problems. The table sums up what changes between them.
| Criterion | Rule-based automation | Chatbot | AI agent |
|---|---|---|---|
| How it works | Follows a fixed script: when this data arrives, take this action | Chats and answers questions from a content base | Receives a goal, chooses the steps and uses tools |
| Input it accepts | Structured data, always in the same format | Free text from whoever is asking | Free text, documents and data from several sources |
| What it delivers | The same action, always the same | An answer in the conversation | A task done or prepared for approval |
| Predictability | High, it does exactly what was programmed | Medium, the answer may vary | Lower, the path can change on each run |
| When to use it | Stable, repeated process | Frequent questions and first-line support | Routine with several steps and simple judgment |
In practice, the three live side by side. Many good workflows use fixed rules where the format is predictable and AI only in the stretch where someone would have to read and interpret. If your question is where to start, the article on how to automate processes with AI deals with exactly this split.
Examples of AI agents used in businesses
The cases where an agent tends to make sense have something in common: the task has several steps, the input arrives in varied formats and the result can be checked by someone before it counts.
- Accounts payable. The agent reads the invoice that arrived by email, extracts supplier, amount and due date, checks it against the purchase order and the purchasing spreadsheet and prepares the entry. Payment is not its job: above a threshold, it becomes an approval request.
- Support triage. It reads the customer's message, checks the history, classifies the subject and proposes the reply. The team is still the one who sends it. If the channel is WhatsApp, the article on WhatsApp chatbots with AI covers this case in detail.
- Document review. Contracts, reports or records are compared with the company's rules, and the agent flags what is missing or inconsistent for a person to review.
- Recurring reports. The agent gathers numbers from different sources, puts together the weekly summary and points out what changed from the previous period.
- Internal support. It answers the team's questions based on the company's procedures and shows the document the answer came from, so whoever asked can check it.
Limits and risks of an AI agent
An agent makes mistakes in a different way from a traditional system. It is worth knowing the risks before connecting any tool to it.
- Errors that look like success. The model may read a field wrong or misunderstand an instruction and move on confidently. That is why the result needs to be checked, at least until the accuracy rate has been measured with real data.
- Unpredictable path. Two runs of the same task may follow different steps. For activities that always require the same result, a fixed rule is usually better.
- Usage cost. Each step of the agent consumes model processing. Long tasks, with many documents or many attempts, cost more than they seem to in testing. Measuring consumption in the pilot avoids surprises.
- Overly broad permissions. An agent with access to everything can cause damage with a single mistake. It should see only the inbox, the folder and the system the task needs, with the lowest possible level of access.
- Malicious content in the input. An email or document may carry hidden instructions trying to steer the agent off course. The environment's rules must take precedence over any text it reads.
- Personal data. Everything the agent reads may be sent to the model provider. This calls for care with Brazil's data protection law (LGPD), the subject of the article on LGPD and artificial intelligence.
Why human review and limits on actions
The safest way to use an agent is to separate what it prepares from what it executes. Reading, extracting, checking and drafting are actions that can be undone without harm. Paying, sending, deleting or changing an official record are actions that need a person in the loop.
In our work, this split is agreed with the team before the first line of code: which actions the agent takes on its own, which become an approval request and for whom, and which are forbidden. Every suggestion and every approval is logged, so you can tell what the AI proposed, who approved it and when. It is the same principle we follow in our way of working: AI speeds things up, a person decides and is accountable.
Limits can also loosen over time. When the accuracy rate of a step stays high for weeks, with real data, it makes sense to discuss whether it still needs approval every time or whether a sample check is enough.
How to get started with AI agents in your company
The path that works is small and measured. An agent that does one task well is worth more than a large project nobody trusts enough to use.
- Pick a single task. Preferably one that is repeated, has volume and has a result that is easy to check. The article on which processes to automate first helps you make that choice.
- Measure how it is done today. How long it takes, how many times a week and where errors happen. Without that number, there is no way to know whether the agent helped.
- Define what it can and cannot do. Tools allowed, data it can read, actions that require approval and who approves.
- Test with real data and review everything. At first, every output goes through a person. That way the accuracy rate shows up for real, not in the demo.
- Expand only after the result. If the gain showed up and errors stayed under control, the next task comes in. If not, adjust before moving on.
In most cases, the agent does not replace the systems you already use. It works connected to them, through an API or through the tools the team already opens every day.