Master Agentic AI
A production-grade curriculum for engineers building AI agents that actually work.
Most agent tutorials show a chatbot calling a tool once. This curriculum teaches you how to build agents that plan, remember, collaborate, and complete real tasks - reliably.
The Curriculum
10 modules. From foundations to production safety - the complete agent engineering stack.
Foundations and Protocols
Who it's for: Engineers starting with agent architectures and the protocols that power them.
| Module | Topics |
|---|---|
| 01 - Agentic AI Foundations | What agents are, ReAct loop, tool use, agent vs. chain, architecture patterns |
| 02 - Model Context Protocol (MCP) | MCP architecture, server/client design, tool registration, resource management |
| 03 - Computer Use Agents | Browser automation, GUI interaction, screenshot parsing, action spaces |
Specialized Agents
Who it's for: Engineers building agents for code, planning, and knowledge-intensive tasks.
| Module | Topics |
|---|---|
| 04 - Coding Agents | Code generation, repository understanding, test-driven agents, IDE integration |
| 05 - Long-Horizon Planning | Task decomposition, plan-and-execute, tree search, replanning strategies |
| 06 - Agent Memory Systems | Short-term, long-term, episodic, semantic memory - retrieval and persistence |
Multi-Agent and Evaluation
Who it's for: Engineers designing multi-agent systems and evaluating agent reliability.
| Module | Topics |
|---|---|
| 07 - Multi-Agent Systems | Orchestration patterns, delegation, debate, consensus, shared state |
| 08 - Agent Evaluation | Trajectory evaluation, task completion metrics, benchmark design, regression testing |
| 09 - Agent Safety and Control | Risk taxonomy, sandboxing, human oversight, permission models, kill switches |
Frameworks in Practice
Who it's for: Engineers choosing and using agent frameworks for production deployments.
| Module | Topics |
|---|---|
| 10 - Agent Frameworks in Practice | LangGraph, CrewAI, AutoGen, Claude Agent SDK - when to use what, trade-offs |
What You Will Be Able to Do
After completing this curriculum:
- Design agent architectures that handle multi-step tasks without falling apart
- Implement MCP servers and clients for tool integration across any platform
- Build memory systems that give agents persistent, retrievable knowledge
- Orchestrate multi-agent systems with proper delegation and state management
- Evaluate agent reliability with trajectory-level metrics, not just final answers
- Deploy agents safely with sandboxing, oversight, and control mechanisms
The Engineering Standard
Every lesson in this curriculum:
- Starts with a real agent failure mode you will encounter in production
- Explains the architecture before showing the framework
- Shows raw implementation first - then the framework equivalent
- Closes with design decisions that test your understanding of agent systems
This is not a tutorial platform. It is an engineering curriculum.
Career Outcomes
Prepared for roles including:
- AI Agent Engineer
- AI Engineer (Agent Systems)
- AI Platform Engineer
- Applied AI Researcher
- AI Infrastructure Engineer
Certification (Coming Soon)
EngineersOfAI - Agentic AI Engineering Certification
Practical. Architecture-focused. Production-ready. For engineers who build agents that complete tasks - not just agents that demo well.