The Agentic AI Engineering Bootcamp
Dr. Ryan Ahmed
Professor & AI Expert (510K+ Students)
Master Agentic AI Engineering & Architect Production-Grade Autonomous Systems
Agentic AI Engineering is 2026's most in-demand skill.
Software is shifting from static systems to AI-native architectures, where autonomous agents reason, plan, and execute complex work.
The problem: Most developers are stuck building fragile API wrappers and they lack the architectural depth to manage state, memory, and multi-agent coordination in real production systems.
The Solution: This bootcamp is your bridge to elite Agentic AI engineering. We move past the basics to architect robust, fault-tolerant agent fleets where specialized agents collaborate autonomously in production.
What makes this course unique?
Production-Grade Architecture: Engineer real systems using LangGraph, CrewAI, & AutoGen.
Automate Agent Workflow Creation: Use Claude Code & Codex to generate, refactor, and evolve agent workflows.
Standardization (MCP): Build secure, interoperable tools using the Model Context Protocol.
Enterprise Reliability: Design for guardrails, persistent memory, observability, & human-in-the-loop safety.
Design & deploy a production-grade, portfolio-ready agentic AI system for real-world use, demonstrating senior-level Agentic AI engineering skills.
What you’ll learn
Become an Agentic AI Engineer by building real-world, production-ready AI agents that autonomously reason and execute complex tasks.
Apply "Problem First" principles to decompose complex problems into scalable, autonomous AI-Native agent architectures.
Build robust single agents via OpenAI SDK with persistent memory structures and state management for reliable, long-term task execution.
Implement strict guardrails and permission layers to enforce operational boundaries, preventing hallucinations and ensuring safety in produc
Design stateful workflows with LangGraph to control cyclic graphs and manage coordination between specialized agent nodes.
Deploy role-specific agent swarms (Planner, Executor) via CrewAI and AutoGen to parallelize tasks and solve problems autonomously.
Build sophisticated handoff mechanisms to transfer context and structured outputs between diverse LLM models without losing state data.
Build standardized MCP servers to give agents secure access to local files and remote databases, decoupling tools from LLM providers.
Extend agent capabilities by coding custom "skills add-ons" and function wrappers to execute complex, domain-specific logic.
Integrate Tavily for real-time search and create custom tools wrapping external APIs to fetch live structured data.
Master Claude Code to autonomously generate complex agentic frameworks, reducing boilerplate and setup time.
Leverage Cursor/Codex to rapidly prototype and debug multi-agent code, using AI to fix logic errors in your orchestration layer.
Accelerate development by treating AI tools as active pair programmers that understand your full codebase and architectural style.
Build "Agentic RAG" pipelines that autonomously plan retrieval, verify source credibility, and synthesize data for accurate answers.
Optimize context windows and hybrid retrieval strategies to handle multi-turn queries without exceeding token limits or budgets.
Connect agents to unstructured data (PDFs, Notion) using reranking to maintain high signal-to-noise ratios in retrieved context.
Deploy agents to production with full observability using tracing tools to debug execution steps and identify bottlenecks in real-time.
Implement evaluation frameworks to benchmark agent performance, ensuring reliability before public release.
Secure agents with privacy-first governance, ensuring compliance with data standards and managing permissions for sensitive actions.
Learn directly from Ryan
Dr. Ryan Ahmed
Professor, AI expert, & YouTuber who has taught 500,000+ learners worldwide.
Who this course is for
Software Engineers & Developers wanting to master state-of-the-art Agentic AI frameworks and build autonomous, production-grade AI agents.
Technical Founders & CTOs who want to architect scalable, fault-tolerant autonomous systems that serve as your product's core engine.
Modern ML & Data Science Practitioners who want to evolve from static models to dynamic Agentic AI workflows that reason, plan, & execute.
Course syllabus
Week 1
Mar
3
Agentic AI Foundations, Build Your First Agent, & Automate Workflow Generation
Agentic AI Fundamentals & Mental Models
Real Agentic AI system architectures in production
Automating Agentic AI Workflow Creation with AI (Claude Code & Codex)
[Build] Assignment 1: Problem Decomposition
Mar
6
Office Hours & Q&A
Week 2
Mar
10
Build Single AI Agents with Tools & Memory
Single Agents Without Memory (and Why They Fail)
Adding Memory, Tools & Guardrails
Context Engineering for Agents
[Build] Assignment 2: Stateful Single Agent with Tools & Memory
Build AI Agents with add-on Skills
Mar
13
Office Hours & Q&A
Schedule
Live sessions
4 hrs / week
Each week begins with a 3-hour live deep-dive where we design, build, and review production-grade agentic AI systems together. On Fridays, we hold a 1-hour live Q&A and office hours session focused on troubleshooting builds, reviewing architectures, and unblocking your capstone progress.
Tue, Mar 3
6:00 PM—9:00 PM (UTC)
Fri, Mar 6
6:00 PM—7:00 PM (UTC)
Tue, Mar 10
5:00 PM—8:00 PM (UTC)
Projects
4 hrs / week
You’ll spend ~4 hours per week building a production-grade agentic AI system end-to-end. Each week builds on the last, from single agents to multi-agent systems, agentic RAG, MCP, deployment, evaluation, and automation. By the end, you’ll ship a portfolio-ready capstone that demonstrates senior-level agentic AI engineering.
Async content
2 hrs / week
You’ll spend ~2 hours per week on focused async content, including short videos, code walkthroughs, and architecture deep dives. This material is designed to front-load concepts and patterns so live sessions can focus on real-world design decisions and hands-on building. All sessions are recorded and available on-demand.
$999
USD