The Complete Agentic AI Engineering Course (2025) Syllabus

A comprehensive, project-driven bootcamp designed to take you from foundational concepts to deploying production-ready AI agents. This course spans over 10 hours of hands-on learning, combining core frameworks, real-world applications, and iterative project development. You'll gain fluency in multiple agentic platforms, build intelligent workflows, and complete eight end-to-end projects that simulate industry challenges—culminating in a capstone where you design and present a full agentic solution. Lifetime access ensures you can learn at your own pace and revisit content as tools evolve.

Module 1: Foundations of Agentic AI

Estimated time: 1 hours

• Core concepts: Tools, Structured Outputs, and Memory patterns
• Introduction to autonomous agent behavior
• Best-practice design patterns for multi-agent collaboration
• Understanding agent roles and responsibilities in workflows

Module 2: OpenAI Agents SDK

Estimated time: 1.25 hours

• Setting up the OpenAI Agents SDK environment
• Creating your first autonomous agent
• Executing and debugging code within agents
• Integrating tools and function calling in agent workflows

Module 3: CrewAI Framework

Estimated time: 1 hours

• Architecting teams of agents for complex workflows
• Defining agent roles, goals, and backstories
• Orchestrating task delegation and handoffs
• Implementing coordination strategies and error handling

Module 4: LangGraph Implementation

Estimated time: 1 hours

• Building graph-based agent pipelines
• Modeling stateful workflows with nodes and edges
• Ensuring robustness and repeatable execution
• Debugging and monitoring agent flow in LangGraph

Module 5: AutoGen AgentChat & Core

Estimated time: 1.25 hours

• Building conversational agents with AutoGen AgentChat
• Implementing feedback loops for self-correction
• Enabling self-improvement via AutoGen Core
• Integrating human-in-the-loop oversight

Module 6: Model Context Protocol (MCP)

Estimated time: 0.75 hours

• Understanding context management at scale
• Integrating Anthropic’s Model Context Protocol (MCP)
• Applying MCP in agentic applications for state control
• Using open-source MCP tools for advanced agent memory

Module 7: Project Labs – Part I

Estimated time: 1.5 hours

• Project 1: Automating customer support workflows
• Project 2: Building an AI research agent with web access
• Project 3: Designing a sales qualification agent team
• Project 4: Deploying an analytics reporting agent pipeline

Module 8: Project Labs – Part II & Capstone

Estimated time: 1.5 hours

• Project 5: Creating a self-improving code generation agent
• Project 6: Building a multi-agent product design system
• Project 7: Implementing a secure financial audit agent
• Project 8: Capstone – design, build, and present a full agentic solution

Prerequisites

• Basic understanding of Python programming
• Familiarity with LLMs and prompt engineering fundamentals
• Access to OpenAI and Anthropic API keys for lab work

What You'll Be Able to Do After

• Apply Agentic AI to real-world commercial problems using proven design patterns
• Architect autonomous agentic solutions with Tools, Structured Outputs, and Memory
• Rapidly build and deploy agents using OpenAI Agents SDK and CrewAI
• Create robust, repeatable pipelines with LangGraph and AutoGen
• Harness MCP for scalable context management in production environments