AI Agents: What they are and how they can work for an SME

Until recently, artificial intelligence waited for a question.
You wrote, it responded, and the conversation ended there.

Today, some systems receive a goal and organize themselves to achieve it; the most recent ones present themselves first, every morning, with a list of what requires your attention.
These are AI agents.

For those leading a company, the useful question concerns time: what hours can they return, to whom, and under what conditions.

In this article, we look at what they are, how they differ from a chatbot, how the new proactive agents work, and how they are already being used in hotels, restaurants, fashion companies, and professional firms.

What are AI agents?

An AI agent is an artificial intelligence system that, upon receiving a goal, independently decides the steps necessary to reach it, uses external tools such as email, calendars, or management software, and completes the task.

Unlike a chatbot, it acts, and within companies, it works under human supervision defined by the user.
If you need a definition to keep on hand, you can find the AI agent entry in our glossary.

A chatbot responds, an agent acts

Think of the difference between a road map and a GPS navigator.

The map gives you information: interpreting it, choosing the path, and correcting the course is up to you.
The navigator has a goal—to get you to your destination—observes traffic in real time, recalculates when it finds a queue, and accompanies you until arrival.

The chatbot is the map; the AI agent is the navigator. You tell it where you want to go, and it finds the way.

The three capabilities that make the difference

Behind an agent, there is almost always a language model—the same technology as ChatGPT or Claude—acting as the decision-making center.

Around that model, three capabilities are added.

• Planning: breaking down a goal into ordered steps.
• Memory: remembering context, preferences, and the outcomes of previous actions.
• Tool use: connecting to the CRM, management software, email, or the web through connections called APIs.

When a provider proposes an “agent,” these three capabilities are the first thing to verify.

What is the difference between a chatbot and an AI agent?

A chatbot answers a question and stops there; an AI agent receives a goal, breaks it down into steps, consults company systems, and completes the work.

The chatbot provides information, the agent produces a result: a sent response, an updated file, a report ready to read.

For an entrepreneur, the distinction has a practical consequence. With a chatbot, the time saved is that of searching for information; with an agent, it is that of the entire operational step.

This is why the right question to ask anyone proposing a solution is: what does it complete on its own, and at what points does it stop to wait for my approval?

How many types of AI agents exist

Agents can be classified in many ways. For an entrepreneur, two matter: how autonomous the agent is and how many agents work together.

By level of autonomy

At the base are simple reflex agents, which follow fixed rules like “if X happens, do Y”: they are suitable for linear automations.

Moving up, there are model-based agents that track context; goal-oriented agents that plan steps toward a target; utility-based agents that choose the best path based on cost, time, or customer satisfaction; and finally, learning agents that improve with experience.

For an SME, the concrete value today lies primarily in goal-oriented agents.

A single agent or a team of agents

A single agent performs a defined task and is the ideal starting point because it is easy to control and measure.

In a multi-agent system, several agents collaborate with distinct roles, coordinated by an agent acting as a department head: it is the right choice for processes that span multiple company functions.

Proactive agents: AI that arrives before the question

The latest evolution concerns when the agent comes into play.

Proactive agents observe connected apps, such as email, calendars, and documents, understand what requires your attention, and propose it to you, with the work already prepared.

You approve, correct, or postpone.

Claude Duet and Meta’s Muse: two different paths

On October 21, 2026, Anthropic opened the research preview of Claude Duet, an app where Claude works as a proactive agent.

It is designed for professionals and small business owners: every morning it sends a brief, flags what requires attention in email and calendars, prepares draft responses, and asks for approval before sensitive operations, such as sending a message or a payment.

At the time of writing (October 2026), it is available only in the United States, via a waitlist, for subscribers to Pro and Max plans.

Meta has chosen another path with Muse, aimed primarily at consumers for activities like travel and shopping. In the same period, OpenAI presented its own proactive agent.

The market is splitting in two directions: assistants for private life and agents for work.
For a company, the second one matters.

Like an executive assistant: prepares, reminds, proposes, and the signature remains yours

Anyone who has worked with a good executive assistant will immediately recognize the model.
The job is the same: filtering mail, remembering deadlines, preparing responses, putting files on the desk to be signed.

The scale changes: the agent works on all digital channels at the same time, at any hour, and reads in seconds what would take a person a whole morning.

For the entrepreneur, this means one specific thing: before activating a proactive agent, the delegation must be written, exactly as was done with an assistant.

What it can do alone, what it proposes to you, what remains exclusively yours.

Where they stop today

The manufacturers themselves describe these tools as early versions.

Anthropic notes that Duet can misinterpret a conversation or incorrectly assess the urgency of a request, and its background checks consume the plan’s quota.

These are limits to be aware of: they reinforce the idea that human supervision is part of the project from day one.

Examples of AI agents in four sectors

Theory becomes clear when it meets a concrete case.
Here is how agents work in four sectors we know well.

Tourism & Hospitality: the agent that handles booking requests

A forty-room boutique hotel receives dozens of emails and messages every day: availability, services, changes, special requests.

A goal-oriented agent, connected to the hotel management software and the inbox, reads the request in any language, verifies real-time availability and rates, composes a personalized response with options and upgrades suitable for that guest, and updates the CRM.

It handles standard requests on its own; large groups and cases outside hotel policies are passed to the reception with a ready-made summary.

The time returned goes to in-person hospitality, where the guest perceives it.

Food & Beverage: the team of agents overseeing reviews and deadlines

A small group of three restaurants, without a marketing office, can rely on three coordinated agents.

• The first listens to reviews on Google, TripAdvisor, and social media, sorting them by tone and urgency.
• The second prepares draft responses consistent with the venue’s style, which the owner approves with a tap.
• The third transforms the daily menu and events into social media posts.

A proactive agent adds a level: it reminds you in advance of deadlines, such as a health permit for a new location, and proposes to prepare the file.

Every response to a critical review only goes out after the owner’s OK.

Fashion & Luxury and Finance & Legal

A fashion brand can use an agent to personalize the relationship with its most loyal customers: it analyzes purchase history, suggests items consistent with the person’s style, and prepares one-to-one communications, while the final touch remains with the stylist.

A professional firm can entrust an agent with the initial reading of documents: it sorts files, extracts key information, and prepares summaries.

Here, caution is paramount, and every decision with legal or financial value remains firmly in the hands of the professional.

Costs, data, and work: what to evaluate first

What time returned and investment depend on

The time an agent returns depends on three factors: how much repetitive work the process contains, how orderly the starting data is, and how many exceptions require human intervention.

The same factors determine the investment, along with the number of integrations with existing systems, the licenses for the chosen tools, and the level of supervision required.

This is why a sensible cost is defined by the process, after analyzing it.
The most reliable way to measure the result is simple: count the hours the process requires before and after.

Data, privacy, and security

An agent works with the information you give it access to, and often processes it in the cloud.

There are three basic rules: grant only the permissions necessary for that task, verify the terms of use for data of each connected service, and provide for human approval for irreversible actions.

For legal and financial firms, professional secrecy must also be evaluated before connecting any archive.

“Will I have to reduce staff?”

This is the question almost every entrepreneur asks, and it deserves an honest answer.
An agent absorbs repetitive and administrative work; judgment, relationships, and creativity remain with people.

The approach we propose uses this space to assign collaborators higher-value tasks and, where possible, to return time: fewer hours spent on activities no one would choose to do, more hours for clients and family.

How to start in three steps

The first step is to choose a repetitive, high-volume process—the one that currently absorbs the most hours without requiring creativity.

The second is to start with a single agent, with a defined indicator from the beginning: hours saved, response times, closed files.

The third is to write the delegation: what the agent does alone, what it proposes to you, what remains for a person.

In the FARO by Factory method, this work corresponds to the Focus phase, which precedes the choice of any tool.

When moving from curiosity to choosing which AI agents for companies to adopt, the starting point is understanding where AI for business creates value in your processes.

If the agent requires custom integrations with your management software, AI software development comes into play.

Frequently asked questions about AI agents