IA agentique vs. assistants : où sont les vrais emplois

A copilot that drafts an email is helpful. An agent that reads a support queue, decides which tickets are refundable, calls the refunds tool, and writes a summary for a human reviewer is a headcount decision. The market has figured out that the second category is where value lives — and where the hiring is happening.

The capability stack behind an agent

An autonomous agent is not magic. It is a loop: an LLM decides the next step, calls a tool, reads the result, and repeats until a goal is met. That loop depends on four concrete skills — function-calling, retrieval-augmented generation (RAG), memory across turns, and orchestration that keeps the whole thing bounded and safe.

• Tool-calling / function-calling: teaching a model to invoke real APIs with structured arguments.
• RAG: grounding the model's answers in your documents instead of its pretraining data.
• Agent memory: giving a multi-step workflow the context it needs to stay coherent.
• Orchestration: deciding which agent runs when, and how to stop it when it goes wrong.

Which roles this unlocks

These skills map directly onto roles the program prepares graduates for. An AI Integration Manager stands up the ecosystem and automates the plumbing. A Data/Business Analyst uses RAG to make internal knowledge queryable. A Project Manager scopes the agent, owns the guardrails, and ships it.

The people getting hired in 2026 are not the ones who can prompt a model. They are the ones who can wire a model into a business process and keep it there.

Where you practice it

Agentic AI is not a single course in this program — it is a through-line. It shows up first in Session 1, where Setting up an AI ecosystem maps the platforms and APIs agents run on, deepens through the Python and automation courses, and culminates in the capstone where students build and defend a full agentic business solution.