AI is showing up in more and more business tools. It can answer a question, write copy, summarize information or help you make sense of data.
But some systems go further. They can use tools, work through several steps and take action to reach a goal.
This is known as agentic AI. In retail, you’ll also hear about AI agents, AI assistants, artificial intelligence agents and agentic retail AI. The terms overlap, but they don’t mean the same thing.
For a store owner, this is a big shift. AI is no longer just there to give answers. It can also help you work directly with your store’s data and tools.
What is agentic AI?
An AI is called agentic when it can act with some degree of freedom to reach a goal.
Depending on the system and the permissions granted, it can:
- understand a request;
- find the relevant information;
- read data or use tools;
- carry out one or more steps;
- prepare or trigger an action;
- use the result to continue its task.
So agency isn’t all-or-nothing. It comes in degrees.
One system may simply call a tool when the user asks. Another may plan and chain several steps to reach a goal. An autonomous AI agent can therefore go further than a basic assistant.
AI assistant vs. AI agent vs. agentic AI: what’s the difference?
The terminology can be confusing, and these aren’t rigid categories.
An AI assistant mainly helps users with their tasks. It’s often conversational, but it can also have features that take action.
An AI agent pursues a goal using the tools and resources available to it. In a store, a retail AI agent can track sales, inventory and alerts.
Agentic AI mainly refers to the ability to reason through steps, use tools and act with varying degrees of freedom.
You could sum it up like this:
Assistant = helps. Agent = acts.
But that’s an oversimplification. An assistant can also use tools and carry out actions.
The real question is: how far can the AI go to complete a task? The table below summarizes the three concepts without treating them as rigid categories. In practice, the lines 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 a goal using the tools available to it | Reasons through the steps needed to reach a goal |
| 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 |
| Level of autonomy | Low by default; depends on what it’s connected to | Varies: can ask for approval before certain actions | Higher, but still bound by the rules and approvals you set |
| Retail example | Explains how to calculate inventory turnover | Reads real stock levels and flags slow-moving items | Chains several steps: spots dead stock, calculates a discount, prepares the promotion |
What does agentic AI change for a store?
Take a simple example.
A store owner wants to know which products are needlessly tying up inventory.
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 in question.
With agentic capabilities and the right permissions, you can then ask it to take the next step, for example:
Prepare a promotion for these products.
The system can then use the tools available to it to prepare or carry out the necessary steps.
That’s how AI gradually moves from answers to action.
Agentic AI relies on your business tools
An AI doesn’t become agentic just because it’s good at analysis.
To act in the real world, it needs access to the right data and tools.
In a store, that can include:
- sales;
- inventory;
- products;
- customers;
- supplier orders;
- quotes and invoices;
- bookings;
- promotions;
- the online store;
- multiple locations at once, for multi-location businesses.
The more connected the setup, the more everyday tasks the AI can take on.
That doesn’t mean it should do everything on its own.
Agentic doesn’t mean fully autonomous
This is a key point.
An agentic AI can have some autonomy without being given carte blanche.
The level of freedom depends on the rules set for each action.
Some tasks can run unassisted. Others may require human approval first.
For example, the AI can analyze sales every morning but ask for approval before changing a price or launching a marketing campaign.
So the challenge isn’t removing human control. It’s deciding which actions you can hand over to the AI.
Example: monitoring store performance
The value becomes clearer with tasks that come 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 results and flag anything worth watching:
- an unusual drop in sales;
- a product that no longer sells;
- a stockout risk;
- a rise in overstock;
- an unusual change in average transaction value.
The AI no longer just waits for a question. It helps monitor the business on a regular basis, based on the tasks you’ve assigned to it.
Gillia: understand, analyze, execute and automate
That’s the approach Gillia takes.
The goal isn’t just to add a chatbot to a store owner’s toolkit.
Gillia lets you work with your store’s data and tools in natural language. You can understand a situation, analyze it and, when the features and permissions allow, trigger the right actions.
In other words, Gillia takes an agentic approach to retail AI: it acts as an AI agent connected to your store.
You can go from a question:
Which products are selling less this month?
to an analysis:
Which ones take up the most stock?
and then to an action:
Prepare a promotion to clear out these items.
How far it can go depends on the tools connected to Gillia and the permissions you give the agent.
Once you’ve approved this kind of action a few times, it can become a routine: the agent then monitors the situation continuously and only checks back with you when something needs your attention. For example:
Prepare this automatically whenever a product slows down like this.
Understand. Analyze. Execute. Automate what should run without you.
This shift from information to action, then from one-off action to continuous monitoring, is the real value of agentic AI in retail. To see what it looks like in practice for your type of business, explore Gillia’s use cases by domain.
For more ideas, see concrete examples of Gillia in action.