AI Programs / Agentic AI in Practice: Building No-Code AI Agents

Agentic AI in Practice: Building No-Code AI Agents

Design, Build, Test, and Govern AI Agents That Get Real Work Done

2 daysNo coding neededApplied AI & Agentic Transformation

Request a ProposalA written scope, plan and fee for your team. See dates Course leaflet (PDF)

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Mostafa Azzam at a conference workshop in Dubai: the AI capstone

Conference workshop, Dubai

Course overview

Why this program

AI is moving from answering questions to getting work done. Agentic AI systems can plan steps, use tools, move information between systems, and complete multi-step tasks, with people approving the decisions that matter. This two-day, intensive, hands-on program shows non-technical professionals how to design, build, test, and govern AI agents using no-code tools.

Participants learn how agents work in practice: triggers, instructions, tools, memory, decision rules, and human approval points. They explore common agent design patterns, from simple assistants to multi-step and multi-agent workflows, and learn how to test agents, handle errors, measure value, and scale what works across the organization.

Pre-built templates, a guided sandbox, and a short pre-course setup checklist ensure every participant can build from the first session, whatever their technical background.

The program culminates in a capstone where participants build and demonstrate a working, multi-step AI agent for a process of their choice, complete with tools, human approvals, and an audit trail.

At a glance

PortfolioApplied AI & Agentic Transformation

Duration2 days

Designed and led byMostafa Azzam

Request a ProposalA written scope, plan and fee for your team. Download the course leaflet (PDF) Request the full course outline

Course objectives

By the end, participants will be able to

01Explain how agentic AI differs from chat-based AI, and where each fits.

02Identify processes that are good candidates for AI agents, and those that are not.

03Design agents with clear goals, instructions, tools, and decision rules.

04Apply common agent design patterns, including multi-step and multi-agent workflows.

05Embed human approval points, audit trails, and error handling in every agent.

06Test, measure, and improve agent performance and business value.

07Build and demonstrate a working, no-code AI agent for a real process.

The learning journey

From understanding to a working build

1 Understand2 Design3 Build4 Test5 Govern

Capstone Project. Build and demonstrate a working, multi-step AI agent with tools, approvals, and an audit trail.

How it runs

This program follows our decision-centered learning design, combining:

Opening Challenges. Practical challenges that surface participants’ assumptions before frameworks are introduced.
Expert-Led Briefings. Concise, evidence-based input on concepts, frameworks, and emerging practice.
Case Studies and Simulations. Realistic situations with genuine choices, where participants decide, defend, and reconsider.
Hands-on Activities. Applying tools and frameworks, including AI tools, to participants’ own work.
Ready-to-Use Tools and Templates. Practical resources delegates can apply immediately in the workplace.