AI no longer just writes and suggests. It spots a problem, reasons about it and acts. For a retail network, this shift is not a matter of convenience: it changes the very nature of what you can delegate. The real question is no longer "should we use AI?" but "how far can we let it act on its own?"
According to Arthur Baribeaud, Lead Data & AI at Digitaleo, mastering agentic AI is becoming a competitive advantage, provided you document, measure and take responsibility for what you delegate. Here are the principles that guide Leo, the AI marketing agent from Digitaleo.
Quick summary: an AI marketing agent differs from an assistant because it acts instead of merely suggesting. To let it act without risk, Digitaleo sets four conditions: the nature of the action, the origin of the data, the reach of the exposure and the verified quality of the result. Security rests on the platform's architecture, and each network chooses its own level of autonomy.
What sets an AI agent apart from an AI assistant?
An assistant suggests, an agent acts
An AI assistant writes a text or suggests an idea, then waits for you to take action. An AI agent goes further: it spots a problem, reasons about the situation and carries out the task itself. This shift from assistance to action is a change in kind, not just in degree.
A connector gives access to data, not business judgment
Plugging a general-purpose AI into your tools lets it read your data. It does not teach it to tell apart a review worth showcasing from one that calls for a human reply. This business judgment, specific to local marketing, is what separates simple data access from a true agent.
Why does a retail network need an AI agent?
Because manual management no longer scales
With several hundred stores, continuously tracking customer reviews, social media posts, location listings and local search visibility by hand is impossible. Each store has its own challenges, and headquarters cannot arbitrate every detail.
Because a cross-functional agent analyzes the whole network and acts location by location
Leo analyzes the performance of your entire network, then acts on each location where the action will have an impact. You keep the big picture without giving up local precision.
What conditions must be met before giving an agent autonomy?
For Arthur Baribeaud, "autonomy is not a box to tick, it is trust built action by action." Four conditions determine how far Leo can act on its own.
1. The nature of the action
Publishing content and addressing a person directly do not have the same impact. The more an action affects an individual customer, the more caution it calls for.
2. The origin of the data
The agent relies only on data that belongs to your network, can be verified and is tied to a specific location. Information from an unclear source has no place in an automated action.
3. The reach of the exposure
The more visible an action is, the more human validation stays in place. Anything that engages your brand's image does not go out without human review.
4. The ability to assess the quality of the result
Quality is assessed before publication, then monitored in production. An action is automated only if you can measure what it produces.
A concrete example: publishing a post that showcases a positive review can be fully automated. Replying to an unhappy customer remains subject to human validation.
Why is prevention better than correction?
Because an undo button comes too late
An "undo" button is reassuring, but it does not really protect you: a deleted post has already been seen. Once inappropriate content is live, the damage to your image is done.
Because upstream filters block the risk before publication
That is why Leo relies on filters placed before the action. The goal is to stop an inappropriate post from going out, rather than cleaning up afterwards.
How can you ensure a franchisee never accesses another location's data?
Through the platform's architecture, not through an instruction
A franchisee must never access another location's data, even by asking the agent. This limit is a structural constraint of the platform, not a simple instruction given in a prompt. An instruction can be bypassed; an architecture cannot be negotiated.
Conclusion
The debate on agentic AI will come down to a simple question: will software vendors be able to document, measure and take responsibility for what they delegate to their agents? At Digitaleo, the answer lies in progressive autonomy: each network chooses its level of delegation and can expand it as trust grows.
With the Digitaleo local marketing platform and its AI agent Leo, you manage the visibility, reviews, social media and local search presence of all your locations, with safeguards built in by design. Digitaleo now supports more than 600 retail networks. Discover Leo and define the level of autonomy that suits your network.
FAQ
What is the difference between an AI assistant and an AI agent?
An AI assistant writes and suggests, but you are the one who acts. An AI agent spots a problem, reasons and acts on its own, within the framework you define for it.
Does Leo publish on its own without human validation?
It depends on the action. A post showcasing a positive review can be fully automated, whereas replying to an unhappy customer remains subject to human validation. Each network chooses its own level of autonomy.
Can a franchisee access another location's data through Leo?
No. This restriction is structural: it is enforced by the platform's architecture and not by a simple instruction given to the agent, even if the franchisee asks for it.
Article adapted from the op-ed by Arthur Baribeaud, Lead Data & AI at Digitaleo, published in Rennes on September 24, 2026.
