Course

Agentic AI

Agentic AI gets much harder once it moves beyond demos and starts operating under real-world constraints. A model that can plan and act still needs structure, reliable inputs, and clear boundaries to perform safely and consistently.

Using Anthropic’s Project Vend as a running case, this course shows what breaks in practice and what must be added to make agent systems dependable. It is a practical introduction to the architecture behind real-world agentic AI.

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What will you learn?

This course examines what happens when AI systems move from chat-based assistance to autonomous action in real business settings. Through the case of Anthropic’s Project Vend, you will see why a single agent can appear capable at first, yet still fail when pricing, inventory, communication, and decision-making unfold under real constraints.
The course then rebuilds the system step by step. You will explore how stronger models, tool use, grounded data, and orchestrator logic improve performance, and why those upgrades still need support from clear role design, governance, and oversight.
It also covers multi-agent setups, memory, retrieval, validation, escalation, and guardrails, showing how these elements work together to make agents more reliable. By the end, you will have a practical framework for understanding how agent systems are designed, where they fail, and how to improve them for real deployment.

Real-world case study

The course is built around Anthropic’s Project Vend, where an AI agent was tasked with running a vending-machine business. This gives you a concrete, realistic thread through the entire course rather than a purely theoretical overview.

From failure to architecture

You will follow the progression from a fragile single-agent setup to a more robust system design. That includes better reasoning, tool integration, orchestration, multi-agent collaboration, memory, and the control mechanisms needed for production use.

Guardrails that matter

A major focus is on governance in practice: constraints, validation workflows, escalation paths, and circuit breakers. The course shows how to reduce risk and limit damage when agents make mistakes, face uncertainty, or encounter manipulation.

Practical systems thinking

Instead of treating agentic AI as “just a better model,” the course explains how models, tools, skills, protocols, memory, and human oversight work together. This helps you understand agent design as a system architecture problem, not only a prompting problem.

Interactive and applied

The course includes a Q&A element and uses concrete examples throughout to make the material easier to apply. The emphasis is on understanding how these design choices affect real deployment outcomes, not just how they look in controlled demos.
Meet the instructor
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Manu De Backer

Professor - Trainer - Expert BA - Process Enthusiast - Smart Process Automation - Academic Director - Product Enthusiast
His focus on business processes is relentless, and as a consultant, he has helped many organizations in optimizing their process-oriented way of working. Driven by a love for great products and customer experiences, he helps organizations thrive in a complex business environment.

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