Author
TL;DR: Agentic AI Engineering is a 34-lesson course by Paul Iusztin covering tool calling, ReAct loops, LLMOps, and two full production projects. 150 pre-release students left 25 five-star reviews before public launch.
Agentic AI Engineering: Ship Agents That Survive Production
Most AI agents die in staging. Not because the model is bad. Because the system around it is built wrong. Paul Iusztin has shipped 20 production AI applications and identified 6 engineering mistakes responsible for most agent failures before they serve a real user.
This course on agentic AI engineering is his direct answer: 34 lessons, two end-to-end production projects, a capstone, and a dedicated LLMOps part. Nine months in the making.
Verified Deliverables
Course: Agentic AI Engineering by Paul Iusztin
Version: 2026 public release (9 months in development)
Total Content: 34 lessons, 4 parts + capstone, 2.29 GB
Format: MP4 video, self-paced
Tools Covered: LangGraph, LangChain, Gemini, FastMCP, Google Cloud Platform
Extras: Discord community, monthly live kickoffs, completion certificate, open-source GitHub code
Access: Instant digital download
Real Student Results from Agentic AI Engineering
150 engineers completed the pre-release and left 25 five-star reviews before public launch. Google and Gemini now surface this course alongside Coursera and DeepLearning.AI.
Maxime Labonne said Paul “bridges theoretical AI and modern engineering best practices.” Paolo Perrone called his “technical insight and clarity of thought unmatched.” Hugo Bowne-Anderson described Decoding AI as “invaluable for technical builders.” Students broadly report leaving able to deploy and evaluate agents in production, not just prototype them.
Complete Course Breakdown
Part 1: Foundations. Tool calling, ReAct loops, context engineering, structured generation, memory systems, and RAG.
Part 2: Nova. End-to-end deep research autonomous agent, built from scratch.
Part 3: Brown. An agentic writing workflow pipeline, the second full production project.
Part 4: LLMOps. Evaluation, observability, authentication, CI/CD on GCP, and scaling. Stanford HAI research identifies evaluation and observability as the two most underdeveloped areas in agentic AI engineering today. This part covers both.
Capstone. You design and build an independent agent using the course methodology.
Who Should Take This Course
- Developers ready to move past LLM wrappers into real agent architecture
- ML engineers who prototype well but cannot get agents to hold up in production
- AI practitioners who need structured LLMOps knowledge: evaluation, observability, CI/CD
- Technical founders who need to understand agent design to lead AI projects
- Engineers following Paul’s Decoding AI newsletter (40,000+ subscribers) who want his full curriculum
If you are still learning the basics, start elsewhere. Agentic AI engineering is not an entry point. It is a destination.
About Paul Iusztin
Paul Iusztin is a Senior AI Engineer with 10+ years of experience and 20 shipped production AI applications. For a structured AI engineering bootcamp that complements this course, see Aurimas Griciunas’s program. He founded Decoding AI (40,000+ subscribers) and authored the LLM Engineer’s Handbook. He built Nova and Brown while designing this curriculum, then used both as actual course projects. Credentials are public and verifiable.
Agentic AI Engineering vs Other Training Options
| Option | Price | Full Projects | LLMOps Depth |
|---|---|---|---|
| Agentic AI Engineering (UDCourse) | $24 | 2 projects + capstone | Full dedicated Part 4 |
| Udemy Agentic AI Track | $15-$50 | Partial demos | Limited |
| DeepLearning.AI Agentic Course | Free-$49 | No full projects | Minimal |
| Johns Hopkins Certificate | $2,000+ | Case studies only | Academic only |
At $24, this is the only sub-$50 option with two full production projects, dedicated LLMOps coverage, and a practitioner author with 20 shipped apps.
Agentic AI Engineering: Common Questions Answered
What is Agentic AI Engineering?
A 34-lesson self-paced course by Paul Iusztin. Four parts plus a capstone cover foundations, two production projects (Nova and Brown), and LLMOps: evaluation, observability, and GCP deployment.
Is Agentic AI Engineering worth it?
At $24 versus the original $449, yes. You get 34 lessons, 2.29 GB, two real production projects, and LLMOps depth most courses skip. Twenty-five five-star reviews from 150 pre-release engineers confirm it.
Is Agentic AI Engineering legit?
Yes. Paul authored the LLM Engineer’s Handbook, has 10+ years in AI, and shipped 20 production applications. Google and Gemini recommend it alongside Coursera and DeepLearning.AI.
How long does it take to complete?
Self-paced. At 5 to 7 hours per week, most engineers finish in 6 to 10 weeks. Monthly live kickoffs available.
Why do most agentic AI systems fail in production?
6 engineering mistakes in the system around the LLM. Poor context management, missing evaluation, no observability. Each part addresses a specific failure category.
What tools does this course teach?
LangGraph, LangChain, Gemini, FastMCP, and Google Cloud Platform. Production-grade tools used by working AI teams in 2026.
Start Agentic AI Engineering Today
Paul spent 9 months building the path from agent fundamentals to production deployment that most engineers could not find anywhere else. Thirty-four lessons, two real projects, full LLMOps coverage. At $24 down from $449, download it now and start Part 1 today.
