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Free preview · 7 full lessons

Agent Engineering Course Preview

Build a research agent and writing workflow connected through MCP in 7 free lessons from the course.

Build Your First Agent
Built for: Software Engineers · Developers · Data Engineers · AI/ML Enthusiasts
Agent Engineering course box with instructors Paul Iusztin and Louis-François Bouchard

Inside the Free Preview

You'll build on a working prototype of the multi-agent system built across the full course: a research agent and writing workflow connected through MCP.

This is a lightweight version designed to show you how the pieces fit together before you build them from scratch.

📩 Want to take this system to production? We'll send you exactly how, watch your inbox and spam.

Diagram of the free preview system: a research agent and writing workflow connected through MCP, with an evaluator-optimizer loop producing the final post

What's Included

7 Full Lessons From the Course

Full lessons with code you can run: from building your first agent prototypes to the foundational techniques that power every production system.

5 Code Lessons With Production Patterns

Context engineering, structured outputs, tool calling, chaining, and routing, each built from scratch, which you can reference, reuse, and build on.

2 MCP Agent Prototypes

A lightweight research agent and writing workflow you can run, inspect, and experiment with. These are starter versions of the production systems you'll build in the full course.

The free preview lessons in Part 1: Foundations of Agents and Workflows

What the Full Course Adds

The full course takes you from prototype to production in 37 lessons. You will build production systems from scratch with LangGraph, ReAct, RAG, persistent memory, multi-agent orchestration, calibrated evals, observability, and GCP cloud deployment, and walk out with what AI engineering roles are hiring for: the skills to ship AI systems and the portfolio to prove it.

01Part 1: Foundations of Agents and Workflows17 items
  1. 1Lesson 0: Build a Research and Writing Agent with MCPFree preview
  2. 2Lesson 1, Part 1: The AI Engineer & The Agent LandscapeFree preview
  3. 3Lesson 1, Part 2: Inside the Systems You BuildFree preview
  4. 4Lesson 1, Part 3: How to Run the Code ExercisesFree preview
  5. 5Lesson 2: LLM Workflows vs. AI Agents -The AI Engineer's DilemmaFree preview
  6. 6Lesson 3: Context EngineeringFree preview
  7. 7Lesson 4: Structured OutputsFree preview
  8. 8Quick Quiz 1Free preview
  9. 9Lesson 5: LLM Workflow PatternsFree preview
  10. 10Lesson 6: ToolsFree preview
  11. 11Lesson 7: Planning and Reasoning
  12. 12Quick Quiz 2
  13. 13Lesson 8: ReAct Practice
  14. 14Lesson 9: RAG Focus
  15. 15Lesson 10: Memory for Agents
  16. 16Lesson 11: Multimodal Data
  17. 17Quick Quiz 3
02Part 2A: Building Agentic Systems; Preparing to Build our Central Research Agent and Writing Workflow5 items
  1. 1Lesson 12: Central Project: Scope & Design
  2. 2Lesson 13: Agent Frameworks Overview & Comparison
  3. 3Lesson 14: LLM Agent System Design Considerations and Framework
  4. 4Running the Agents
  5. 5Quick Quiz 4
03Part 2B: Building Agentic Systems; Building our Central Research Agent6 items
  1. 1Lesson 15: Nova End-to-End Project Walkthrough
  2. 2Lesson 16: Foundations of Agentic Systems with FastMCP
  3. 3Lesson 17: Initial Data Ingestion and Tooling
  4. 4Lesson 18: The Research Loop: Query Generation, Perplexity, and Human Feedback
  5. 5Lesson 19: Final Outputs and Agent Completion
  6. 6Quick Quiz 5
04Part 2C: Building Agentic Systems; Building our Central Writing Workflow8 items
  1. 1Lesson 20: Brown End-to-End Project Walkthrough
  2. 2Lesson 21: Behind the Scenes of Iterating AI Architectures with the Brown Writing Agent
  3. 3Lesson 22: Implementing the Foundations of the Writing Workflow
  4. 4Lesson 23: Reviewing and Editing Through the Evaluator-Optimizer Pattern
  5. 5Lesson 24: Human-in-the-Loop Through MCP Servers
  6. 6Lesson 25: Orchestrate and Integrate Our Capstone Agents
  7. 7Quick Quiz 6
  8. 8Lesson 26: End-to-End Demo: Generating a Course Lesson
05Part 3: Evaluation, Observability, Optimizations, and Deployment12 items
  1. 1Lesson 27: Agent Observability with Opik
  2. 2Lesson 28: Creating Datasets for AI Evals
  3. 3Lesson 29: Defining the Evaluation Processes and Metrics Theory
  4. 4Lesson 30: Evaluating the Writing Workflow
  5. 5Quick Quiz 7
  6. 6Lesson 31: Continuous Integration for AI Engineering
  7. 7Lesson 32: Preparing For Deployment: Authentication and Docker
  8. 8Lesson 33: Preparing For Deployment: Database and File Download/Upload
  9. 9Lesson 34: Continuous Deployment for AI Engineering
  10. 10Lesson 35: MCP vs. Skills vs. CLI
  11. 11Lesson 36: Creating Proper Agent Skills
  12. 12Quick Quiz 8
06Part 4: Instructions for Students Capstone Project and Certification3 items
  1. 1Part 4: Building Your Own MCP Server (To Receive Certification)
  2. 2Part 4: Project Submission
  3. 3Share your success and showcase your new skills!
07Extra tools1 item
  1. 1AI Tutor - Ask any question!
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