WE_IA_AGENT_BUS

AI AGENTS - Master Class

Become an "Agent Manager" in half a day. The era of simple chatbots is over. Where generative AI merely responds, Agentic AI perceives, reasons, and autonomously executes complex tasks. In 3.5 intensive hours (60% theory / 40% practice), go from user to architect of autonomous solutions. What you will master: The technological breakthrough (understand the fundamental difference between an Assistant who knows and an Agent who acts), cognitive architecture (learn to build the "brain" of an agent via the Perception → Reasoning → Action loop and the use of external tools), real practice (design and deploy an agent on a concrete use case), industrial reliability (master the strategies to avoid hallucinations and infinite loops to secure your processes).

WEnvision
✓ Official training WEnvisionLevel Intermediate⏱️ 0.5 day (3.5h)

What you will learn

  • Distinguish Generative AI from Agentic AI, from automated to autonomous.
  • Distinguish AI Assistants from AI Agents and understand their respective capabilities.
  • Master the "Perception, Reasoning, Action" cycle for designing AI agents.
  • Identify the maturity levels of Agentic AI and their concrete applications.
  • Structure an agentic use case, from use case definition to deployment and monitoring.
  • Anticipate the benefits, risks, and key success factors related to deploying AI agents in the enterprise.

Prerequisites

  • Knowledge of the fundamental concepts of Artificial Intelligence and Machine Learning is a plus.
  • Having the equivalent level of content provided by an "Introduction to Generative AI" type training.
  • A computer with internet access.

Target audience

  • Business & IT managers, Digital Directors, Data Directors and Product Managers looking to steer their projects and business towards AI and/or manage native agentic products, Professionals with initial exposure to AI or machine learning who want to master the concepts of agentic AI

Training Program

3 modules to master the fundamentals

Topics covered
  • →From automated AI to autonomous AI: what really changes
  • →Difference between conversational assistants and autonomous AI agents
  • →Key principles of agentic AI: perception, reasoning, action
  • →Maturity levels of agentic AI in organizations
  • →Market status and current dynamics
  • →Impacts on operational models, roles, and governance
Topics covered
  • →Identifying a domain where agentic AI provides a competitive advantage (business, support functions, operations)
  • →Methodology for selecting and prioritizing high-impact use cases
  • →How to define a level of autonomy and control
  • →Design framework for an AI agent (objectives, data, tools, scenarios)
  • →Expected benefits: performance, quality, deadlines, resilience
  • →Risks, limitations, and conditions for success (security, compliance, acceptance)
  • →Overview of market solutions and selection criteria
Topics covered
  • →Choosing a real use case from the enterprise context
  • →Formalizing the agent's logic and its action loop
  • →Designing autonomously managed scenarios
  • →Visualizing the agent's operation and its interactions
  • →Evaluating results: value created, observed limitations, improvement levers
  • →Conditions for scaling and integration into existing systems
Activities

Hands-on workshop: design and manage an AI agent on a concrete use case

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Upcoming sessions

May 27, 2026
Distanciel • Français
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August 24, 2026
Distanciel • Français
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November 16, 2026
Distanciel • Français
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Quality Process

SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach

Teaching Methods Used
  • Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
  • Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
  • Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
Evaluation and Monitoring System

The achievement of training objectives is evaluated at multiple levels to ensure quality:

  • Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
  • Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
  • Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.

395€ excl. VAT

per learner