Agentic & Generative AI Engineering

4.5 (300–500+ Learners)

Learn from industry experts with hands-on projects & live mentorship

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30 Sessions

Duration – 60 Hours

English / Arabic

Certificate of Completion

Fundamentals of agentic AI and autonomous systems

Designing and building AI agents with reasoning and decision-making abilities

Integrating AI agents with APIs, tools, and external data sources

Building real-world use cases such as chatbots, automation agents, and workflows

Best practices for deployment, optimization, and ethical AI usage

Overview of Generative AI and LLMs

Working with Retrieval Augmented Generation (RAG)

Basics of AI Agents

Integrating Agents with 3rd party apps

Building AI Agents- Technical Deepdive

Production deployment of AI Agents

Participants will understand how RAG works incl mechanics of each stage in a RAG pipeline

Participants will learn how to test/ evaluate RAG and Agentic AI Apps/ systems

This is a hands-on program so the learnings can be carried forward for core development & testing tasks in project and IP/Practice related work

About the Course & Benefits

The Agentic AI Engineering Program is designed to equip learners with the skills to build intelligent, autonomous AI agents that can reason, plan, and act independently. The course blends foundational AI concepts with hands-on implementation, enabling participants to design real-world AI-driven workflows and applications using modern tools and frameworks.

Benefits included:

KHDA + Medilearn certification

Gain hands-on experience building and deploying AI agents

Understand RAG pipelines and evaluation of AI systems

Apply AI agents for real-world supply chain and enterprise

Develop skills for post-production monitoring, optimization, and testing

Course Circulation

Module 1

Module 2

Module 3

Module 4

Module 5

Module 6

MediLearn Certificate

Earn a Course Completion Certificate, an official credential from MediLearn, recognizing your dedication and hard work throughout the program. This certificate confirms that you have successfully met all course requirements, including assessments, practical activities, and guided learning sessions. It serves as a trusted proof of your newly acquired knowledge and skills, valued by employers and professional institutions. Display it with pride on your resume, LinkedIn profile, or portfolio to strengthen your professional credibility and career opportunities.

Student Success Stories

“Education is one of the most essential and valuable assets that an individual can possess, It plays a pivotal role in shaping.”

The Career Changer
"This course was the key to my career pivot. I went from a traditional IT role to an AI Specialist at a leading tech firm in Dubai. The hands-on GCP and Gemini API projects were exactly what employers were looking for."

The Promotion Earner
"After completing the Agentic AI course, I was able to lead a major project using Vertex AI, which directly led to my promotion to Senior Data Scientist. The practical skills are invaluable."

The Technical Expert
"As a backend engineer, I needed to upskill in AI to stay relevant. This course's deep dive into Google Gemini API on GCP was exactly what I needed to secure a new, higher-paying role."

The Strategic Leader
"To lead my team effectively, I needed to understand the strategic applications of agentic AI. This course gave me the knowledge to develop a new AI strategy that got me promoted to Director of Technology."

The Innovator
"I've always been interested in AI, but this course gave me the practical skills to actually build solutions. I was able to land a job at a cool tech startup in Dubai, working on cutting-edge projects."

The Specialist
"The healthcare sector is rapidly adopting AI. This course's focus on Google's advanced AI tools helped me secure a specialized role in medical data analysis, a field I'm passionate about."

FAQs

What is Agentic AI Engineering?

Agentic AI Engineering focuses on designing AI systems that can plan, reason, use tools, and autonomously execute multi-step tasks in real-world environments.

How is Agentic AI different from traditional AI systems?

Traditional AI responds to inputs, while Agentic AI can decide actions, call APIs, manage memory, and adapt its behavior based on outcomes.

What skills are required to build Agentic AI systems?

Key skills include prompt engineering, system design, API integration, cloud infrastructure, model evaluation, and monitoring autonomous behavior.

What are common use cases for Agentic AI Engineering?

Use cases include workflow automation, AI copilots, research agents, decision-support systems, and enterprise task orchestration.

How do engineers ensure reliability and control?

Reliability is achieved through guardrails, human-in-the-loop approvals, fallback logic, logging, and continuous evaluation of agent decisions.

Can Agentic AI systems be deployed in production?

Yes. With proper architecture, monitoring, security, and cost controls, Agentic AI systems can be safely deployed and scaled in production environments.

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