What is an AI agent and how does it work?
The term "AI agent" is everywhere, often used vaguely. Yet, the distinction from a simple chatbot is concrete and important for deciding where and how to use it in business. This article explains, without unnecessary jargon, what an agent is, how it works, and why human control remains central.
From chatbot to agent
A chatbot answers a question based on its model and, at best, a few documents. It doesn't "do" anything: it produces text. An agent goes further—it pursues a goal by chaining together steps and using tools. Where the chatbot tells you how to do something, the agent does it: it creates a record, fills in a field, sends a notification, queries a database, triggers an action in another system.
The cycle: perceive, reason, act
In practice, an agent operates in a loop:
- Perceive. It receives context: a request, a document, an event, the state of a system.
- Reason and plan. Based on this objective, it breaks down the problem, decides on the steps, and chooses which tools to use. This is where the language model brings flexibility: it adapts to situations not explicitly foreseen.
- Act via tools. The agent calls on "tools"—functions or APIs provided by the developer: reading a file, creating a CRM lead, extracting fields from a PDF, sending an email. These tools are the bridge between reasoning and the real world.
- Observe and iterate. It observes the result of its action and continues, until it reaches the goal or a point where a human must decide.
Tools: where the value lies
An agent without tools is just a verbose chatbot. What makes it useful is the set of tools it is given and the safeguards that frame them. Well-designed, these tools are narrow and explicit: "create a lead with these fields," "read this type of document," "suggest a classification." You don't give an agent unlimited access; you give it exactly the capabilities needed for its task, and nothing more.
Human control (human-in-the-loop)
A good agent deployment does not seek total autonomy. For high-stakes decisions—a client commitment, a payment, a submission to an authority—the agent prepares and the human decides. This architecture is not a weakness: it's what makes the agent adoptable in regulated professions, and what protects the company. The agent earns its place by being reliable and traceable, not by acting without supervision.
Memory, knowledge, and data
A business agent often relies on a knowledge base (internal documents, procedures) via augmented search techniques, and on memory of the ongoing conversation or case. The central question then becomes: where does this data live? For a Swiss company, running the agent on a controlled infrastructure—for example, Azure in Switzerland, with the agent components of Azure AI Foundry—makes it possible to reconcile capability and confidentiality.
When an agent is the right tool
An agent is relevant when a task is repetitive, involves several steps and systems, and a degree of judgment can be framed. It is less so when a simple rule suffices, or when the decision is purely human and cannot be delegated. The right reflex is not "let's put an agent everywhere," but "which specific step would benefit from being automatically prepared, under control?"
In summary, an AI agent is a system that pursues a goal by perceiving, reasoning, acting through tools, and remaining under human supervision for what matters. It is this combination—and not the model alone—that creates value in business.
Safeguards: as important as capabilities
Designing an agent means defining what it cannot do as much as what it can do. You limit its tools, validate its inputs and outputs, require human review for binding actions, and log everything. These safeguards do not unnecessarily restrict the agent: they are the condition for entrusting it with real data and actions. A powerful agent without safeguards is a risk; a well-framed agent is a trustworthy digital collaborator.
A concrete end-to-end example
Let's take an incoming quote request. The agent perceives the message, reasons to identify missing information, asks a few questions, then acts: it creates a lead in the CRM with the collected context and prepares an initial estimate. It does not send it to the client—it forwards it to a colleague who reviews, adjusts, and approves it. In a few steps, a repetitive task is automatically prepared, documented, and left under control. This is exactly the kind of workflow Houle implements for service companies, on a controlled infrastructure in Switzerland.