AI agents:
beyond conversation.

AI agents are systems that can use artificial intelligence models together with tools, data and procedures to pursue defined goals. What they can do depends on how they were designed and on the permissions they are given.

Not all agents are autonomous in the same way: some need frequent confirmation, others operate within preset limits.

An agent is usually made up of four elements:

• A language model, which interprets the request and decides the next step.
• Instructions, which define the goal, rules and limits.
• Tools, meaning the actions it can take: searching a document, reading a database, calling an API, preparing a draft.
• Context and memory, meaning the information it can access while working.

The agent repeats a loop: it assesses the situation, chooses a tool, observes the result and decides whether to continue or stop. The more tools and permissions it has, the more important it is to design the checks well.

An AI chatbot mainly offers a conversational interface. An automation runs a sequence of operations according to predefined rules. An agent can combine AI models and tools to choose actions within a controlled process.

In practice these categories can overlap. That’s why it is useful to look at how a system behaves rather than at its marketing label.

Agents can be designed to consult documents, retrieve information, support operational workflows or interact with software systems. Each application requires an assessment of the available data, the permissions and the quality of the answers.

In manufacturing companies, for example, one could evaluate an assistant that answers customers about the status of an order by checking the management system, or an agent that reads an order email and prepares a draft for a person to review. These are possibilities to design and test, not ready-made solutions.

Many processes don’t need an agent: if the steps are always the same, a fixed-rule automation is simpler, more predictable and cheaper. An agent can make sense when:

• requests arrive in free form and differ every time (emails, documents, questions);
• the path to follow changes depending on the content;
• several sources need to be consulted before acting;
• there is a clear way to check the result before it has any effect.

Using external tools introduces risks: unwanted operations, excessive permissions, incorrect information, malicious content and improper handling of data.

Human oversight, testing, observability and least-privilege access are central to the design.

AI agents are part of our study and experimentation. We do not yet present them as an established commercial solution or as a proprietary product.

When we have a complete demonstration project, we will document it in our portfolio with its architecture, checks and limitations.