Rafael Pierre

Cohort 1

August 2025 Cohort

January 2026

October 2025 Cohort

A Python project demonstrating the use of OpenAI GPT models and MCP (Model Context Protocol) agents to fetch and process real-world data. Clone this repo right away and start hacking!

Github Repo: Build Your First Agentic AI App with MCP

Understand the hype behind Model Context Protocol (MCP), what are its main architecture components, and visualize the main components of an Agentic AI App backed by MCP.

Building Your First Agentic AI App with MCP: Lightning Lesson Slides

Engineers and engineering leaders who build, deploy, and scale software systems.

Data scientists who apply statistical and ML techniques to extract insights from data.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Build and deploy agentic apps in production from day one with Azure, OpenAI Agents SDK, Docker, FastAPI and Phoenix Arize.

AI Engineer with 17+ years experience - ex-Hugging Face, Databricks

From Vibe to Live: Build and Deploy Production AI Agents

Senior Principal Data Scientist

developer

Founder

ML Engineer

Data Scientist

technical account manager

Solution Architect - Data & AI

Applied Math

Principal Software Engineer

Most courses stop at building toy notebooks. Here, you’ll learn to architect, evaluate, and deploy AI agents that solve real business challenges-factoring in security, scalability and operational costs. You’ll master not just the “how”, but the “when” and “why” behind agentic AI, with a special focus on software engineering, observability and scalability best practices. Production-Ready, Not Just Toy Projects Starting on the first day of this course, this is what you will work on: ✅ Building agentic apps with&nbsp;OpenAI Agents SDK✅ Building observability pipelines with&nbsp;OpenTelemetry&nbsp;and&nbsp;Phoenix Arize&nbsp;to track what your agents are doing and understand why✅ Containerizing and deploying your agentic application using&nbsp;Docker&nbsp;and&nbsp;FastAPI✅ Implementing robust guardrails, so that responses from your agents are relevant and not prone to hallucination or prompt injection attacks ✨ Biggest differentiators of this course Hands-On Product DevelopmentFrom day one, you’ll build a real-world agentic product, guided by step-by-step tutorials. You’ll gain practical experience that goes far beyond theory Free OpenAI CreditsGet USD 400 in free GPT 4.1 credits as soon as you enroll into this course Individual Coaching SessionsFeel free to schedule 30 minute individual sessions with me every week. You will get direct, personalised help, making sure you never lose momentum. Industry Guest Lectures &amp; Expert WorkshopsLearn directly from the people shaping the future of AI. This cohort features guest lectures and workshops with industry experts, bringing you what's newest in AI Agent features for developing, evaluating and deploying AI Agents. Capstone Project: From Idea to ProductionYou’ll connect best practices with hands-on implementation. For your capstone, you’ll design, evaluate, and deploy a production-grade AI agent. You will showcase your ability to bridge the gap between theory and enterprise impact. Is this course for me? 👉🏻 If you have ever struggled with any of the following challenges: 🔹 Prototype Graveyard: You have built simple AI demos that never made it to production🔹 Evaluation Paralysis: You struggle to measure if your agents are actually performing well🔹 Security Vulnerabilities: You need to mitigate the risk that your agents are exposed to prompt injection attacks🔹 Availability &amp; Resiliency: You want to prevent agentic AI products from breaking after being deployed Then yes, this course is for you. 🚀 What you will have built by the end of this course ✅ An end-to-end multiagent RAG system with clear agent scopes, tool calls and guardrails✅ A production-deployed AI agent with a public URL you can share with employers✅ Comprehensive observability pipelines to keep track of your agent's metrics and availability✅ Portfolio-ready project demonstrating end-to-end agent development capability 🚀 Tools you will use 🦾 OpenAI Agents SDK for robust multiagent application development and orchestration⚡️ Redis for lightning fast agentic memory implementation📊 Phoenix Arize for end-to-end agent observability and evaluation⚙️ Github Actions for seamless CICD pipeline orchestration🐳 Docker for containerization and reproducible development✨ FastAPI for rapid Python backend development 🟢 Who this course is for 1. Software Engineers, Machine Learning Engineers, and Data ScientistsYou have solid experience with data pipelines and classical ML problems. You’re ready to integrate large language models into agent architectures, build workflows using tool integrations, memory management, and task planning for practical AI systems. 2. Product Managers and AI StrategistsYou translate business objectives into technical solutions. You want to understand how to define AI agent functionalities from product requirements, establish meaningful success metrics, and effectively collaborate with engineering teams to deliver scalable, user-centric agent applications. 3. Tech Leads and Backend DevelopersYou oversee complex systems involving multiple AI agents and services. You seek to master best practices for orchestrating, monitoring, and scaling agent workflows that run reliably in production environments. 🔴 Who this course is not for 1. Absolute Beginners in Programming or Software DevelopmentThis course assumes proficiency in Python and coding fundamentals. If you’re new to programming, start with foundational development courses before progressing here. 2. People looking for research heavy contentThis practical course leverages pre-built language models and focuses on implementation, not on transformer internals, model training, or research heavy topics. 3. No-Code or Low-Code DevelopersThis program is code-centric, involving APIs and orchestration through programming libraries. If you prefer drag-and-drop or visual tools, this course may be more technical than expected 💸 Course Value vs InvestmentTotal Value: $3200 vs Investment: $1000 🔷 Live and on-demand training to help you design, build and deploy real AI Agent Apps($1000 value) 🔷 Personalized support via Slack plus weekly 1x1 sessions (30 min x 4)($800 value) 🔷 Free OpenAI Credits($400 value) 🔷 Complimentary Access to All Future Cohorts($1000 value) Student Testimonials ﻿🚀 "I truly appreciate our trainer Rafael. He has outstanding experience in AI, and I’m really glad to have him as my mentor. I’ve learned so much from his training sessions and the 1:1 online guidance. He clarified my doubts and explained how to build robust agentic AI systems with best practices something you normally don’t get from other courses. I’m definitely going to enroll in the upcoming programs. I highly recommend this program to anyone who wants to gain broader and deeper knowledge in AI." 🚀 "Glad to be in the first cohort! Agents development, functional tasks, openai SDK, evaluation techniques, observability and much much more. Thank you Rafa!" 🚀 "Got good understanding on how to build agentic apps. Best part of this course is the observability platform." Ready to go beyond the PoC? Join us and build AI agents that matter-powered by real-world tools, enterprise best practices, and the support you need to succeed.

Design, build and deploy production grade AI Agents from day one

Course syllabus

This course is highly hands-on, so basic Python knowledge is required.

Basic Python knowledge

You use AI and Gen AI on a daily basis, and/or you are familiar with the main use cases surrounding it

Familiarity with AI & Gen AI

No notebook demos! You'll code, move fast and break stuff.

Willingness to code full fledged products

Recorded videos, live lessons, office hours and hands-on projects

Fridays

Course schedule

Software Engineers, MLEs, and Data Scientists who want to build agentic apps using function calling, short term memory and observability

POs and PMs who want to dive deep into Agentic System architecture to deliver user-centric agentic apps in collaboration with engineers

Consultants, Solution Architects and Field Engineers who want to learn what it takes to successfully run agentic apps in production

Who is this course for

What you’ll get out of this course

Theory is important: you will learn foundational concepts around AI Agents, but you will also start coding and shipping Agentic AI products from the first day.

Build an AI Agent on Day One

You will have access to a private Slack workspace where you can ask questions to me - or to fellow students. Want to chat, code together or brainstorm? Feel free to book a weekly 30 min 1x1 with me.

Personalized Guidance

You will get USD 400 in free OpenAI GPT 4.1 credits to build your agents. These credits are yours to use even after you finish the course.

Free GPT Credits

Evaluating AI Agents can be overwhelming. We will show you what to evaluate, how to do it and what to do with these metrics.

Master and Implement AI Agent Evaluation

Should I go for vanilla RAG? Should I implement Agentic RAG instead? You will deep dive into the main use cases for Agentic RAG, what problem it solves, when it should be used, and when it shouldn't.

Understand where Agentic RAG shines, and use this knowledge to design and build unique AI products

Learn about the best practices around tracking LLM calls, tools and agent interactions. Master prompt injection attacks and how to mitigate them. Incorporate all these concepts in your app.

AgentOps: Apply AI security and observability best practices

Set up cloud resources, add authentication, deploy tracing and guardrails, release your app’s code and share your live project with the world

Deploy your Agentic AI App Into Production and showcase your skills

You will learn from some of the best engineers and product professionals in the industry. In previous cohorts, we had guest speakers from Microsoft, LlamaIndex and Replit - stay tuned for the full speaker lineup for this cohort!

Learn directly from Rafael Pierre

Rafael Pierre

Courses

From Vibe to Live: Build and Deploy Production AI Agents

Lightning Lessons

Vibe Code and Deploy Your Agentic App

Build Agentic App UIs with AI: 30 Minute Frontend Sprint

Build Your First Agentic AI App with MCP

Resources

Building Your First Agentic AI App with MCP: Lightning Lesson Slides

Github Repo: Build Your First Agentic AI App with MCP