Building AI Agents Cohort 2

AI Agents masterclass

Objective: Equip participants with both practical skills and a solid conceptual foundation to design, build, and deploy AI-powered automation agents. This includes hands-on experience with Large Language Models (LLMs), n8n workflows, and Retrieval-Augmented Generation (RAG) systems. Participants will also learn how to integrate these agents ethically and securely into messaging platforms, websites, and enterprise environments for real-world impact.

Why it matters

1.       AI agents and automation are transforming business operations, customer support, and internal workflows.

2.       RAG (Retrieval-Augmented Generation) enhances LLMs by allowing them to access private or real-time knowledge sources - key for accurate, context-rich responses.

3.       Tools like n8n democratize workflow automation by lowering the code barrier.

4.       Companies are increasingly looking to integrate AI into their products and customer touchpoints.

5.       Prompt engineering is no longer a fringe skill—it’s becoming as foundational as SQL or Python scripting in AI-driven workflows.

Prerequisites

·       Basic Understanding of AI & LLMs

·       System Requirements (Docker or Node.js (v18+) and npm if running locally without Docker) for n8n setup

·       API Accounts (OpenAI API key)

What You’ll Gain

By the end of this masterclass, you will:

·       Build fully functional AI automation agents using n8n and LLM APIs

·       Implement RAG pipelines for accurate, real-time, and domain-specific responses

·       Learn to connect agents to messaging platforms, websites, and secure environment

·       Understand ethical AI integration, data privacy, and responsible deployment practices

·       Gain confidence in designing workflows that save time, reduce costs, and delight users

AI agents Course Outline

 Meet Our Trainer

Ezra Muinde

Ezra Muinde is an AI Mastery, Agentic AI Systems, and Data Science specialist who bridges the gap between advanced AI capabilities and real-world business execution. He builds production-grade AI systems; including dashboards, autonomous agents, and data pipelines and then trains teams to independently design, deploy, and maintain similar solutions. His approach is implementation-first and battle-tested, with every technique proven in live client environments under real constraints of cost, scale, and reliability. He is also a Master’s student in Data Science at the University of Nairobi, strengthening his practice with rigorous academic grounding while delivering measurable, production-ready AI outcomes for organizations.