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Master the transition from AI prototypes to production-ready systems and build a high-impact portfolio as a certified Agentic AI Engineer.
File Size: 2.29 GB.
Paul Iusztin – Agentic AI Engineering
To Agent or Not to Agent?
Most developers get stuck in three places when building agents. We teach you to navigate all three.
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Agents Work Locally, Break in Production
Build two production agents, fully deployed
Build Research Agent and Writing Workflow with full evaluation systems, observability, and deployment pipelines. These are production templates you can adapt for market research, content & report generation, diagrams, documentation, or any domain where autonomous intelligence and automations with agentic patterns add value. -
Not Sure When Agents Are Worth It
Master the decision framework
Learn to design workflows that stay maintainable as requirements evolve. Master when to use deterministic routing versus agent autonomy, when to keep humans in the loop, and the patterns that scale from prototype to production. -
Skills Get Outdated Fast
Build mental models, not framework knowledge
We teach system design fundamentals from scratch, not just today’s frameworks. Graduate with two deployable agents, certification, and the engineering judgment to architect any AI system, regardless of which tools are trending next.
Build Your First Agent This Week
• Own two agents, fully production-ready
• Gain portfolio-ready skills with certification
• Master fundamentals that outlast trends
Course Curriculum
1. Part 1: Foundations of Agents and Workflows
Lesson 1, Part 1: The AI Engineer & The Agent Landscape
Lesson 1, Part 2: Course Admin
Lesson 2: LLM Workflows vs. AI Agents -The AI Engineer’s Dilemma
Lesson 3: Context Engineering
Lesson 4: Structured Outputs
Quick Quiz 1
Lesson 5: LLM Workflow Patterns
Lesson 6: Tools
Lesson 7: Planning and Reasoning
Quick Quiz 2
Lesson 8: ReAct Practice
Lesson 9: RAG Focus
Lesson 10: Memory for Agents
Lesson 11: Multimodal Data
Quick Quiz 3
2. Part 2A: Building Agentic Systems; Preparing to Build our Central Research Agent and Writing Workflow
Lesson 12: Central Project: Scope & Design
Lesson 13: Agent Frameworks Overview & Comparison
Lesson 14: LLM Agent System Design Considerations and Framework
Running the Agents
Quick Quiz 4
3. Part 2B: Building Agentic Systems; Building our Central Research Agent
Lesson 15: Nova End-to-End Project Walkthrough
Lesson 16: Foundations of Agentic Systems with FastMCP
Lesson 17: Initial Data Ingestion and Tooling
Lesson 18: The Research Loop: Query Generation, Perplexity, and Human Feedback
Lesson 19: Final Outputs and Agent Completion
Quick Quiz 5
4. Part 2C: Building Agentic Systems; Building our Central Writing Workflow
Lesson 20: Brown End-to-End Project Walkthrough
Lesson 21: Behind the Scenes of Iterating AI Architectures with the Brown Writing Agent
Lesson 22: Implementing the Foundations of the Writing Workflow
Lesson 23: Reviewing and Editing Through the Evaluator-Optimizer Pattern
Lesson 24: Human-in-the-Loop Through MCP Servers
Lesson 25: Orchestrate and Integrate Our Capstone Agents
Quick Quiz 6
Lesson 26: End-to-End Demo: Generating a Course Lesson
5. Part 3: Evaluation, Observability, Optimizations, and Deployment
Lesson 27: Agent Observability with Opik
Lesson 28: Creating Datasets for AI Evals
Lesson 29: Defining the Evaluation Processes and Metrics Theory
Lesson 30: Evaluating the Writing Workflow
Quick Quiz 7
Lesson 31: Continuous Integration for AI Engineering
Lesson 32: Preparing For Deployment: Authentication and Docker
Lesson 33: Preparing For Deployment: Database and File Download/Upload
Lesson 34: Continuous Deployment for AI Engineering
Quick Quiz 8
6. Part 4: Instructions for Students Capstone Project and Certification
Part 4: Building Your Own MCP Server (To Receive Certification)
Part 4: Project Submission
Share your success and showcase your new skills!
7. Extra tools
AI Tutor – Ask any question!
How It Works
Project-based learning designed for working professionals—self-paced with live instructor support.
- Self-Paced + Live Introductory Cohort Calls: Learn on your schedule with lifetime access. Join introductory cohort calls with instructors and fellow course takers.
- Learn by Building: Hands-on projects from day one. Build two production agents from scratch, not toy examples or follow-along tutorials.
- Active Discord Community: Get unstuck fast. Connect with fellow builders, share progress, and collaborate on projects in an active engineering community.
- Certificate Upon Completion: Prove you built and deployed production AI. Graduate with certification backed by two real systems—portfolio-ready proof you ship.
- Lifetime Access + Updates: All current content plus future updates as AI evolves. Your investment stays valuable.
- Money-Back Guarantee: 30-day full refund, no questions asked. Risk-free enrollment.
Who This Course Is For
Serious learners who want to sweat a bit and build cool shit.
- Python: Comfortable with functions, classes, and API calls
- LLMs: Familiar with OpenAI/Claude APIs and basic prompting
- Docker & Cloud: Understand containerization and deployment basics
- Skill Level: Intermediate to advanced, expect a challenge
- Mindset: Learn by building, not watching tutorials
Brought to you by
Towards AI’s mission is to make “building with AI” more accessible.
Since 2019, we have helped teach over 400k about AI with our courses, blogs, tutorials, books, community, and personalized consultancy.
After shipping 21 AI apps, Paul Iusztin has used his best-selling LLM Engineer’s Handbook, Decoding AI Magazine, and social media platforms to lead 162,000+ AI engineers out of “demo purgatory” and into production-grade engineering.
Course Features
- Lecture 0
- Quiz 0
- Duration 10 weeks
- Skill level All levels
- Language English
- Students 38
- Assessments Yes