Course description
AI Agents are the next evolution in software engineering — and this course teaches you how to build them from the ground up. You'll go from zero to building full-stack, production-ready AI agents using the most in-demand tools in the industry: LangChain, LangGraph, MCP, A2A, Pinecone, OpenAI, Anthropic Claude, and Ollama.
This isn't a theory course. Every lesson is project-based. You'll build 4 complete applications — each with a React frontend, Node.js backend, and real AI integrations. You'll start by building an AI Personal Assistant with RAG and PDF ingestion, then create your own MCP tool server, then architect multi-agent systems using both LangGraph and the A2A protocol, and finally build an AI Career Assistant powered by Anthropic's Claude.
Whether you're a frontend developer, backend engineer, or full-stack builder — if you want to add AI agents to your skillset and build applications that actually think, reason, and take action — this is the course for you.
Who is this course for?
Software Engineers
You write code and want to add AI agents to your stack.
Full-Stack Developers
You want to build AI-powered products, not just consume APIs.
Career Switchers
You have coding experience and want to break into AI agent engineering.
Course curriculum
4 projects · 6 modules
Foundations
- Welcome to the courseFree preview
- Who is this course for?Free preview
- Programming language & coding methodologyFree preview
- Curriculum Tour
- Environment setup
- The AI Pyramid
- What are AI Agents?
- RAG vs Agentic RAG
- AI Agent Tech Stack
AI Personal Assistant
- Setting up LangChain with OpenAI
- PDF ingestion and text chunking
- Vector embeddings with Pinecone
- Building the RAG pipeline
- ReAct agent pattern with tools
- Switching between RAG, API, and MCP modes
- Running locally with Ollama
- React chat interface and full-stack integration
MCP Search Server
- Understanding MCP architecture
- Building a tool server with the MCP SDK
- Implementing Streamable HTTP transport
- Implementing stdio transport
- Exposing web search and image search tools
- MCP vs API — when to use each
AI Trip Planner — LangGraph
- Introduction to LangGraph state machines
- Designing the trip planning state graph
- Building search, budget, and itinerary agents
- Parallel agent orchestration
- Real-time SSE streaming
- Connecting MCP tools to LangGraph agents
AI Trip Planner — A2A Protocol
- Introduction to the A2A protocol
- Agent discovery with .well-known/agent.json
- Building independent agent services
- JSON-RPC communication between agents
- Building the orchestrator
- Comparing LangGraph vs A2A architectures
AI Career Assistant
- Integrating Anthropic's Claude API
- Building the resume analyzer agent
- Building the market researcher agent
- Building the gap analyst agent
- LangGraph pipeline with SSE streaming
- Full-stack integration and deployment
What you'll learn in this course
- Build an AI Personal Assistant with RAG — ingest PDFs into a Pinecone vector database and answer questions using retrieval-augmented generation
- Master the Model Context Protocol (MCP) — build your own tool server with Streamable HTTP and stdio transports
- Understand when to use MCP vs API and the trade-offs of each approach
- Build multi-agent systems with LangGraph — orchestrate parallel search, budget, and itinerary agents through state graphs
- Implement the A2A (Agent-to-Agent) protocol — distributed agents communicating via JSON-RPC with discovery endpoints
- Build the same app twice with two different architectures (LangGraph vs A2A) to learn when to use each pattern
- Integrate with both OpenAI (GPT-4o) and Anthropic (Claude) — and run models locally with Ollama for offline, privacy-first development
- Build an AI Career Assistant — a multi-agent pipeline with resume analysis, market research, and gap identification powered by Claude
- Implement real-time streaming with Server-Sent Events (SSE) for live progress updates in your AI applications
- Ship complete full-stack apps — React frontends, Node.js/Express backends, and production-ready AI pipelines you can deploy and put in your portfolio
Included: The AI Agent Engineering Handbook
60-page companion guide
Architecture patterns, best practices, code references, and everything you need beyond the videos. Use it as a reference while you build, and long after you finish the course.
Included with enrollment
Downloadable Resources
- AI Agent Engineering | Roadmap A step-by-step learning path from fundamentals to production-ready AI agents. PDF
- AI Agent Engineering | Curriculum Full breakdown of every module, lesson, and project covered in the course. PDF
- AI Agent Engineering | Glossary Key terms and definitions — RAG, MCP, A2A, embeddings, vector stores, and more. PDF
- AI Agent Tech Stack | Cheat Sheet Quick-reference guide to every tool and framework used in the course. PDF
- AI Agent Engineering Handbook The complete companion guide — architecture patterns, best practices, and code references. PDF
Course price:
$89.99
LangChain
LangGraph
OpenAI
Anthropic
MCP
A2A
Ollama
Pinecone
React
Node.js
What's included:
- 8 hours of lectures
- 66 lectures
- 4 real projects with full source code
- Video lectures in Arabic
- 4 downloadable PDF resources
- AI Agent Engineering Handbook (60 pages)
- Lifetime access
- Certificate on completion
