CF

Agentic AI Engineering Course

7h 33m 4s
English
Paid

Learn how to build agent systems that work in real production. This course guides you step by step. You will design, test, and ship real agents that you can add to your portfolio.

What You Will Learn

You will learn when to use an agent and when a simple workflow is enough. You will also learn how to build agents that run well in production, not only on your machine.

When an Agent Helps

  • Know when to use an agent. Avoid the three common mistakes developers make when they pick an agent for a task.
  • Fix local‑to‑prod issues. Build two full agents with deployment, logs, and clear pipelines.
  • Break down complex tasks. Build a research agent and a content system with clear checks and tool use.
  • Make good decisions. Choose between rules and agent freedom. Pick points where a human should review work. Create clean prototypes you can scale.
  • Learn long‑lasting skills. Focus on system design, not trends. You will finish with two deployed agents and the core skills to build more.

What You Will Build

Research Agent

  • Collect and shape data from the web, GitHub, and YouTube.
  • Run research in clear cycles.
  • Use tools for search, parsing, and analysis.

Content Workflow

  • Create text, diagrams, and images.
  • Run automated checks for quality.
  • Keep style and context stable.
  • Ship work through a clean pipeline.

Agent Architecture Skills

  • Plan and run stable workflows.
  • Build custom checks and monitors.
  • Deploy with Docker and cloud tools.
  • Work with Cursor, Claude, and other tools.

Course Outcome

  • Two production agents in your portfolio.
  • A clear view of design ideas that outlast frameworks.

Who This Course Is For

  • Python users who know functions, classes, and APIs.
  • People who know basic LLM use with OpenAI or Claude.
  • People who know Docker and simple deployment ideas.
  • Learners who like to build through practice, not long videos.

Additional

Attention! The course contains many text-based lessons, while only videos are available in the player. Be sure to download all materials and alternate between watching videos and reading text lessons,

About the Authors

Louis-François Bouchard

Louis-François Bouchard thumbnail

Louis-François Bouchard is a French-Canadian AI engineer and educator behind the What's AI newsletter and YouTube channel — one of the more accessible explainer sources on modern AI research. He is also the lead instructor for several courses on the Towards AI platform, where he teaches the production-engineering side of LLM applications.

His CourseFlix listing carries six Louis-François Bouchard courses spanning the applied AI track: Building LLMs for Production, 10-Hour LLM Fundamentals, Build Your First Product with LLMs / Prompting / RAG, Master AI for Work, Beginner Python Primer for AI Engineering, and the Agentic AI Engineering Course.

Material is paid and aimed at engineers picking up applied LLM work as a serious skill. For broader content, see CourseFlix's LLMs & Fundamentals, RAG, and AI Agents category pages.

Paul Iusztin

Paul Iusztin thumbnail

Paul Iusztin is a Romanian ML engineer and AI educator, the author of LLM Engineer's Handbook (Packt) — one of the more widely-read modern textbooks on production LLM engineering — and the host of the Decoding ML newsletter. His material focuses on the engineering side of taking LLMs from notebook experiments to production systems.

His CourseFlix listing carries two Paul Iusztin courses: LLM Engineer's Handbook (the book / course companion) and the Agentic AI Engineering Course. Together the courses cover the production-engineering arc from training and fine-tuning LLMs through deploying agentic systems.

Material is paid and aimed at engineers picking up production LLM and agentic-system work as a serious skill rather than dabbling. For broader content, see CourseFlix's LLMs & Fundamentals and AI Agents category pages.

Towards AI

Towards AI thumbnail

Towards AI is one of the larger AI-focused publishers on the open web — originally a Medium publication and now a multi-author content platform plus a paid course catalog focused on production LLM engineering. The brand has tracked the post-ChatGPT generative-AI wave from inside the field rather than from a generic SaaS-marketing perspective.

The CourseFlix listing reflects their applied focus: Building LLMs for Production, 10-Hour LLM Fundamentals, Build Your First Product with LLMs, Prompting, RAG, the Agentic AI Engineering Course, Beginner Python Primer for AI Engineering, and Master AI for Work. Material is paid and aimed at engineers who already know Python and want to ship production AI features rather than read a survey of the field.

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#1: Lesson 1, Part 1: The AI Engineer & The Agent Landscape
All Course Lessons (11)
#Lesson TitleDurationAccess
1
Lesson 1, Part 1: The AI Engineer & The Agent Landscape Demo
18:41
2
Lesson 2: LLM Workflows vs. AI Agents -The AI Engineer's Dilemma
20:37
3
Lesson 7: Planning and Reasoning
16:18
4
Lesson 9: RAG Focus
09:41
5
Running the Agents
34:31
6
Lesson 15: Nova End-to-End Project Walkthrough
39:59
7
Lesson 20: Brown End-to-End Project Walkthrough
58:36
8
Lesson 21: Behind the Scenes of Iterating AI Architectures with the Brown Writing Agent
01:09:49
9
Lesson 26: End-to-End Demo: Generating a Course Lesson
01:07:31
10
Lesson 27: Agent Observability with Opik
01:14:37
11
Lesson 28: Creating Datasets for AI Evals
42:44
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Frequently asked questions

What prerequisites do I need before enrolling in this course?
Before enrolling in the Agentic AI Engineering Course, you should have a basic understanding of programming and familiarity with concepts in AI and machine learning. Experience with tools such as Docker and cloud platforms will be beneficial since deploying agents using these technologies is part of the curriculum.
What projects will I complete during the course?
The course includes building two main projects: a Research Agent and a Content Workflow system. The Research Agent involves collecting and shaping data from sources like the web, GitHub, and YouTube. The Content Workflow focuses on creating text, diagrams, and images, running automated quality checks, and ensuring style consistency.
Who is the target audience for this course?
This course is designed for developers and engineers interested in learning how to build and deploy AI agents in production environments. It is suitable for those who want to build practical skills in agent architecture and are looking to add real agents to their professional portfolio.
How does the course depth compare to similar AI courses?
The Agentic AI Engineering Course offers a practical approach to agent design and deployment, focusing on real-world applications. Unlike some AI courses that may focus on theoretical aspects, this course emphasizes system design, decision-making between rules and agent freedom, and creating scalable prototypes.
What specific tools or platforms are covered in the course?
The course covers deploying agents using Docker and cloud tools. It also involves working with Cursor, Claude, and other tools for search, parsing, and analysis. These tools are integral to building stable workflows and deploying agents effectively.
What topics are not covered in the course?
The course does not cover basic AI and machine learning theory in depth. It assumes a foundational knowledge in these areas and focuses instead on the practical aspects of designing, testing, and deploying AI agents.
How does this course provide value for future careers?
The skills gained from this course, such as system design, deploying agents in production, and working with modern tools, are transferable to various roles in AI engineering and development. Completing the course with two deployed agents enhances your portfolio, demonstrating your ability to handle complex agent systems in a professional setting.