AI is increasingly present in business tools. It can answer a question, write a text, summarize information or help read data.
But some systems go further. They can use tools, follow several steps and act to reach a goal.
This is known as agentic AI. In retail, people also talk about AI agents, AI assistants, artificial intelligence agents or agentic retail AI. The terms overlap, but they do not mean the same thing.
For a merchant, this shift matters a lot. AI is no longer only there to give an answer. It can also help work on the store’s data and tools.
What is agentic AI?
An AI is called agentic when it can act with a certain level of freedom to reach a goal.
Depending on the system and the permissions granted, it can:
- understand a request;
- find the useful information;
- read data or use tools;
- carry out one or more steps;
- prepare or launch an action;
- use the result to continue its task.
Agency is therefore not a simple yes or no. There are several levels of agency.
One system can simply call a tool at the user’s request. Another can plan and chain several steps to reach a goal. An autonomous AI agent can therefore go further than a simple assistant.
AI assistant, AI agent, agentic AI: what is the difference?
The words can be confusing. They do not describe closed boxes.
An AI assistant mainly helps the user with their tasks. It is often conversational, but it can also have action functions.
An AI agent pursues an objective using the tools and resources within its reach. In a store, a retail AI agent can track sales, inventory and alerts.
Agentic AI mainly refers to the ability to reason about steps, use tools and act with more or less freedom.
It can be summed up like this:
Assistant = helps. Agent = acts.
But this summary is too simple. An assistant can also use tools and carry out actions.
The real question is rather: how far can the AI go to carry out a task? The table below summarizes the three notions without turning them into closed boxes: in practice, the boundaries shift depending on the tools the AI has access to.
| AI assistant | AI agent | Agentic AI | |
|---|---|---|---|
| What it does | Answers, explains, writes — often in conversation | Pursues an objective using the tools within its reach | Reasons about the steps needed to reach an objective |
| Ability to act | Can also act, if given tools | Acts by design: reads data, triggers actions | Decides on its own which tools to use and in what order of steps |
| Level of autonomy | Low by default, depends on what it is connected to | Variable: can ask for validation before certain actions | Higher, but still bounded by the rules and validations defined |
| Retail example | Explains how to calculate inventory turnover | Reads actual stock and flags low-turnover items | Chains several steps: spots dead stock, calculates a discount, prepares the promotion |
What does this change for a store?
Take a simple example.
A merchant wants to know which products are needlessly tying up their stock.
A conversational AI can explain how to calculate inventory turnover or which metrics to track.
An AI connected to the store’s data can go further. It can answer a request like:
Which products have had no sales in 60 days and take up the most stock?
It can then read sales and stock levels to find the items concerned.
With agentic capabilities and the necessary permissions, the user can then ask for one more action, for example:
Prepare a promotion on these products.
The system can then use the tools at its disposal to prepare or carry out the useful steps.
This is how we move, little by little, from answers to action.
Agentic AI relies on the company’s tools
An AI does not become agentic just because it does good analysis.
To act in the real world, it must be able to access the useful data and tools.
In a store, this can include:
- sales;
- inventory;
- products;
- customers;
- supplier orders;
- quotes and invoices;
- bookings;
- promotions;
- the online store;
- several locations at once, for a multi-site business.
The more connected the setup, the more the AI can act on everyday tasks.
This does not mean it should do everything on its own.
Agentic does not mean fully autonomous
This is a key point.
An agentic AI can have some autonomy without having carte blanche.
The level of freedom depends on the rules set for each action.
Some tasks can be done without help. Others may require human validation before the action.
For example, the AI can analyze sales every morning but ask for validation before changing a price or launching a marketing campaign.
The challenge is therefore not to remove human control, but to decide which actions can be entrusted to the AI.
Example: tracking a store’s activity
The value becomes clearer when the task comes up often.
Instead of asking every week:
Is there anything unusual in my sales?
you can turn this analysis into a routine.
The AI can then read the data, compare the results and flag something to watch:
- an abnormal drop in sales;
- a product that no longer sells;
- a stockout risk;
- an increase in tied-up stock;
- an abnormal change in average basket.
The AI no longer just waits for a question. It takes part in the regular monitoring of the business according to the tasks entrusted to it.
Gillia: understand, analyze, execute and automate
This is the logic Gillia follows.
The goal is not just to add a chatbot to the merchant’s tools.
Gillia lets you act in natural language on the store’s data and tools. The merchant can thus understand a situation, analyze it and, when the functions and permissions allow, launch the useful actions.
Gillia therefore follows an agentic retail AI approach. It acts as an AI agent connected to the store.
A merchant can thus move from a question:
Which products are selling less well this month?
to an analysis:
Which ones take up the most stock?
and then to an action:
Prepare a promotion to clear these items.
The level of action depends on the tools connected to Gillia and the permissions given to the agent.
Once an action of this kind has been validated several times, it can become a routine: the agent then monitors the situation continuously and only comes back to the merchant when an action deserves their attention, for example:
Prepare this operation automatically whenever a product slows down in this way.
Understand. Analyze. Execute. Automate what should keep going without you.
This shift from information to action, then from one-off action to continuous monitoring, is the main value of agentic AI for retail. To see what this looks like in practice for your line of business, explore Gillia’s uses sorted by domain.
To go further, see concrete examples of Gillia uses.