Artificial intelligence can answer a question, write a text or analyze data. An AI agent goes further when it can pursue a goal using the information and tools made available to it: consulting data, choosing the necessary steps, using a function, preparing an operation and, when authorized, carrying it out.
In a store, this can mean moving from a question like:
“Which products are likely to run out this week?”
to:
“Prepare the restocking for the products concerned.”
The important difference is therefore not simply that the AI “talks” or “acts.” It lies in its ability to draw on data and tools to complete a task in several steps.
What is an AI agent?
An AI agent is a system able to pursue a goal by combining an artificial intelligence model with data, tools and execution rules. Depending on its configuration, it can, for example:
- understand a request written in natural language;
- look up the information it needs;
- analyze a situation;
- determine the useful steps;
- use one or more tools;
- check certain results;
- prepare or carry out an action.
Take a simple request:
“Which products should I probably reorder this week?”
To answer correctly, the agent may need to check inventory, analyze recent sales, review orders already in progress and take supplier lead times into account. If it then has a purchasing tool, it can also prepare a proposed order. The value of the agent therefore comes from its ability to chain several operations around a single goal.
AI agent, AI assistant and chatbot: what is the difference?
These terms are often used loosely.
A chatbot
A chatbot is above all a conversational interface. It receives a message and provides a reply. By itself, that says nothing about the technical capabilities behind it.
An AI assistant
An AI assistant helps a user complete a task. It can write, explain, analyze and, depending on the product, use certain tools. A modern assistant can therefore already have agentic capabilities.
An AI agent
The term “agent” puts more emphasis on the system’s ability to pursue a goal by using tools and chaining steps, with a variable degree of autonomy.
The boundary is therefore not absolute. The same product can be conversational, act as an assistant for some uses and rely on agentic mechanisms for others. This is more accurate than contrasting:
assistant = answers
with:
agent = acts
because this distinction is now too simplistic — we go into detail in our article AI assistant, AI agent and agentic AI: what is the difference.
What is agentic AI?
The term agentic AI generally refers to systems in which the AI does not simply produce a single answer. It can take part in a multi-step loop:
understand the goal → choose an action → use a tool → observe the result → continue or adjust.
This does not necessarily mean it acts without human oversight. The degree of autonomy can vary. An agent can:
- simply prepare an operation;
- ask for confirmation before carrying it out;
- carry out certain authorized actions;
- run automatically for precisely defined tasks.
Agentic therefore does not mean “without control.”
What is an AI agent used for in retail?
Its value depends on the data and tools it can actually access. Here are several concrete uses — you will find more in our overview of concrete uses of AI and our guide to running your business with AI.
Analyzing sales
The merchant can ask:
“Compare my sales this week with last week’s.”
The agent can query the available data and compare:
- revenue;
- number of receipts;
- average basket;
- categories;
- items;
- periods.
The next question can be:
“Which categories contribute most to the gap?”
The agent then keeps the context and digs deeper into the analysis. It is important, however, to distinguish what the data shows from what it can actually explain. A simultaneous drop in a category and in revenue does not necessarily prove why customers bought less.
Monitoring inventory
Another request can be:
“Which products are likely to run out before the next restocking?”
To produce a useful estimate, several pieces of information may be needed:
- available stock;
- sales rate;
- orders in progress;
- supplier lead time;
- safety stock.
The agent can cross-reference this data to highlight the items to watch. The result remains an estimate based on the available information: future sales or actual lead times may change.
Preparing a restock
Once the items are identified:
“Prepare the matching restock.”
The agent can use the available data to build a proposal. Depending on how the business operates, it may need to take into account:
- quantities available;
- expected sales;
- orders already placed;
- pack sizes;
- minimum order quantities;
- lead times;
- target stock.
A proposal can then be reviewed before it is created or sent.
Working with customers
An agent connected to customer data can answer a request like:
“Which regular customers haven’t bought anything in 90 days?”
It can then refine:
“Among them, keep those who made at least six purchases in the previous twelve months.”
Then prepare an action:
“Write a reactivation message for this segment.”
The value lies in keeping the context from search → segmentation → action preparation.
Working with documents
Agents can also work from documents when the necessary functions are available. For example:
“Find the latest invoice from this supplier.”
or:
“Extract the items and quantities shown on this delivery note.”
or even:
“Prepare the invoice for this accepted quote.”
However, extraction and verification must be distinguished. Reading “12 units” on a delivery note does not certify that 12 units were physically received.
Managing operations in business tools
When an agent has tools that let it write to business systems, it can go beyond analysis. For example:
“Create the product record for this item.”
“Prepare a discount on this selection.”
“Turn this accepted quote into an invoice.”
“Change this booking.”
“Prepare the supplier order.”
Actual execution then depends on:
- the available tools;
- the permissions granted;
- the rules defined;
- the planned level of validation.
An AI agent does not spontaneously have all these powers: they are given to it by the systems it is connected to.
How does an AI agent complete a task?
There is no single universal way of working. An agent can nevertheless follow logic like this:
- Understand the goal“Prepare the restock for at-risk products.”
- Identify the information neededStock, sales, orders in progress, supplier lead times…
- Use the available toolsIt queries the relevant systems.
- Analyze the resultsIt identifies the items that match the criteria.
- Prepare or carry out the next stepIt can prepare an order, ask for missing information or ask for confirmation.
- Check the resultDepending on the system, it can verify that the operation was carried out or use its result to continue the task.
It is this ability to chain several steps depending on what happens that makes the system agentic.
Is an AI agent autonomous?
The answer depends on what you mean by autonomy. An agent can have some autonomy in choosing how to complete a task without being authorized to decide every action on its own. For example, it may freely look up several pieces of data but have to ask for confirmation before sending a supplier order. Conversely, a low-risk routine can be authorized to run without validation each time.
There are therefore several levels:
Assistance
The AI prepares.
Validation
The AI proposes and the user confirms.
Limited delegation
Certain defined actions are authorized.
Automation
A precisely bounded task can run on a recurring basis — see also our article on task automation for a store.
The appropriate level depends in particular on the risk and reversibility of the action.
What data should an AI agent use?
An agent is only useful if it can access the information its task requires. In a store, this can include:
- sales;
- inventory;
- catalog;
- customers;
- supplier orders;
- quotes;
- invoices;
- bookings;
- e-commerce data.
But more data does not automatically mean better results. Access must be relevant, reliable and properly authorized. An AI with erroneous data can produce an erroneous analysis faster.
Do you need to replace your software to use an AI agent?
Not necessarily. Two architectures are possible.
The agent is built into the business software
The data and functions are already available in the same environment.
The agent is connected to existing tools
It uses integrations or APIs to view or modify the necessary information.
In both cases, the principle is similar: the agent needs a reliable way to access the data and functions it needs. The user experience can then hide part of this complexity by letting you simply phrase your request.
AI agent or traditional automation?
An AI agent is not always the best solution. For a simple rule:
If stock < 10, send an alert.
traditional automation is perfectly suited. For a request like:
“Analyze sales, identify the items whose sales rate is speeding up and prepare those likely to run out before the next delivery.”
AI can become more interesting because the task requires more interpretation and coordination between several pieces of information. The two technologies can also work together:
an automation triggers the task; the agent performs the analysis; a rule controls the final action.
To learn more about how they complement each other, see our dedicated article on task automation in retail.
What limitations should you know about?
An AI agent remains dependent on its environment.
Incomplete data
It cannot properly analyze information it does not have.
Incorrect data
An inventory error can skew a restocking recommendation.
Misinterpretation
An ambiguous request can lead to a different action than the one expected.
Excessive permissions
Giving an agent more access than necessary increases the potential consequences of a mistake.
Irreversible actions
Some operations require more control than others.
Uncertainty
An analysis or forecast can carry a margin of error.
An effective agent is therefore defined not only by what it is able to do, but also by how its capabilities are governed.
How does Gillia work as an AI agent for retail?
Gillia applies this logic to the retail context. It can use the functions and data available in its environment: POS, inventory, catalog, online store, quotes and invoicing, bookings or customer loyalty.
The merchant can start with a question:
“Which products are at risk of a stockout this week?”
then continue:
“Prepare the matching restock.”
Or:
“Which regular customers haven’t bought in three months?”
then:
“Prepare a reactivation action for this segment.”
Depending on the operation and the permissions defined, the action can be prepared, submitted for validation or built into a routine. The goal is to allow a natural flow:
understand → analyze → act
while keeping a level of control suited to the action.
An AI agent is above all a new way of using your tools
The most interesting change may not be that AI can do “everything.” It is that it can become a layer between the user and several systems. Instead of having to know:
- which report to open;
- which filter to apply;
- where to find the document;
- which screen to use;
- which data to bring together;
the merchant can start by expressing their goal. The agent then translates that intent into operations in the tools it has. This removes neither business software, nor management rules, nor the need for reliable data. It mainly changes how you run them. You can find more examples of concrete uses of Gillia for your business.