AI Agents - Master Class
Become an "Agent Manager" in half a day. The era of simple chatbots is over. Where generative AI just responds, Agentic AI perceives, reasons, and executes complex tasks autonomously. In 3.5 intensive hours (60% theory / 40% practice), move 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 does). 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 strategies to avoid hallucinations and infinite loops to secure your processes.
What you will learn
- Distinguish Generative AI from Agentic AI, from automation to autonomy
- Distinguish AI Assistants from AI Agents and understand their respective capabilities
- Master the "Perception, Reasoning, Action" cycle for AI agent design
- Identify maturity levels of Agentic AI and their concrete applications
- Structure an agentic use case, from definition to deployment and monitoring
- Anticipate the benefits, risks, and key success factors related to deploying AI agents in business
Prerequisites
- Knowledge of fundamental concepts of Artificial Intelligence and Machine Learning is a plus
- It is essential to have the equivalent level of content from an "Introduction to Generative AI" type training
Target audience
- Business & IT managers, Digital Directors, Data Directors and Product Managers wishing to orient their projects and business towards AI and/or pilot native agentic products, as well as those with prior exposure to AI or machine learning wishing to master agentic AI concepts.
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 state and current dynamics
- →Impacts on operational models, roles and governance
Topics covered
- →Identify a domain where agentic AI provides competitive advantage (business, support functions, operations)
- →Methodology for selecting and prioritizing high-impact use cases
- →How to define a level of autonomy and control
- →Framework for designing an AI agent (objectives, data, tools, scenarios)
- →Expected benefits: performance, quality, deadlines, resilience
- →Risks, limits and success conditions (security, compliance, acceptance)
- →Overview of market solutions and selection criteria
Topics covered
- →Selection of a real use case from the business context
- →Formalization of agent logic and action loop
- →Design of autonomously managed scenarios
- →Visualization of agent operation and interactions
- →Evaluation of results: value created, observed limits, improvement levers
- →Conditions for scaling and integration into existing systems
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
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Guided Labs — Guided practical exercises on software, hardware, or technical environments.
- Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
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.
Train multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote