AI agents in business: what they actually do

An AI agent is a program that is given a goal, decides for itself which steps to take to reach it, and acts inside your tools: it reads a document, checks a database, drafts a reply, updates a record. In a business setting, it is useful wherever a model’s intelligence can help, including on tasks that vary from one case to the next, provided a person validates anything that commits the company.

The term is used everywhere, but it covers very different realities, from a simple chat assistant to a system that handles cases from start to finish.

This article sorts that out: what an AI agent does, what it does not do, how it is put into production, and what drives its cost.

AI agent, chatbot, automation: what’s the difference

A chatbot answers a question. It waits for someone to prompt it, generates a text response, then stops.

A traditional automation follows a path defined in advance: if a given field contains this value, then send that message. It stays reliable as long as the situation fits the rules, and it breaks down as soon as it falls outside them.

An AI agent sits between the two. You give it a goal and a set of tools it can access. It decides the order of actions, reads what it finds, then adjusts what to do next. For a customer request, for example, it can retrieve the order, check its status, review the applicable terms, and prepare a reply, without every branch having been mapped out in advance.

That autonomy is what makes it useful. It is also why it needs guardrails.

What an agent actually does in a company

In practice, an agent is good at:

• reading loosely structured documents, such as emails, PDFs, or forms, and pulling out the useful information
• looking up information across several tools and cross-checking it
• preparing a reply, a summary, or a case file based on your own content
• routing a request to the right person or the right workflow
• filling in or updating a record in a business tool

What it should not do on its own:

• making a decision that commits the company, such as a payment, a refusal, or a contractual commitment
• settling a case it is not confident about, which it should hand over to a person instead
• taking action in a tool it has not been explicitly authorized to use

In a serious project, a person approves anything that commits the company and the agent acts on its own for the rest. Over time, what it handles on its own can expand, but that decision should be based on measurement, not on a feeling.

Use cases for an SME

In an SME, the best first use cases often follow the same pattern: requests that vary from one case to the next, information scattered across several tools, and someone spending time finding it and pulling it together.

A few typical examples:

• incoming requests: read incoming messages, identify the topic, find the relevant file, and prepare a first reply
• documents to review: check that a file includes the expected documents and flag what is missing
• search across internal documents: answer team questions based on the company’s procedures, contracts, or technical datasheets, with the source for each answer
• data entry between two tools: take information written in an email or a PDF and enter it into the management tool.

Choosing the right process matters more than choosing the tool. To assess whether a process is ready, read our article on when to automate a business process with AI. And if the question goes beyond agents, we have also written about AI in business and the work it changes.

Creating an AI agent: the steps on the company side

Creating an AI agent takes more than connecting a model to a mailbox. The steps that really matter mostly happen on the company side:

1. Choose a narrow scope. One type of request, one workflow, one team. Not “the entire customer service department.”
2. Gather the data and the rules. Reference documents, the history of past cases, and the rules the team applies even when they have never been formally documented.
3. Define what the agent is allowed to do. Which tools it can read from, which ones it can write to, and which actions always require approval.
4. Plan for human review. Who checks the output, at what stage, and how the agent is corrected when it gets something wrong.
5. Test it on real cases. Compare what the agent suggests with what the team would have done, using a known sample.
6. Move to production and monitor it. Log what the agent does, track errors, and adjust.

The last step is often the most underestimated. A demo that works on ten examples says very little about what will happen across hundreds of real requests.

What affects the cost of an AI agent

There is no standard price for an AI agent. The cost mainly depends on the following points:

• the number of tools to connect and how easily their data can be accessed
• the condition of the starting data: well-organized documents and usable history, or cleanup work needed first
• the volume of requests handled, which affects model usage costs
• the level of control required: validation for every action, logging, traceability
• hosting and security requirements, for example a requirement to keep data in Europe
• post-launch support: monitoring, fixes, updates

The safest approach is to start with a pilot on a limited scope, with a measurable objective, before expanding.

Public funding may also cover part of the project: see our guide to digital grants in France.

When an agent is not the right answer

An agent is not always the right tool. It is better to avoid one when:

• the process follows fixed, well-known rules: a standard automation will do the job more simply and at a lower cost
• no one can describe what a good outcome looks like: the team itself does not agree on what counts as a good answer. A messy case is not a problem for an agent, but without agreement on the expected answer, its work cannot be evaluated
• each error is very costly and no validation is possible.

In those cases, the first step is often to clarify the process, not to add artificial intelligence.

Deploying an AI agent to production with Perfusion

At Perfusion, we design and deploy AI automations, including agents, on an AWS environment hosted in Europe. We start with a scoping phase, then a pilot within a limited scope, with human validation for anything that could commit the business, before extending the automation one process at a time.

To see how we work, take a look at our AI automation on AWS offering (page in French).