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Your Path to Agentic AI for Production

Become an Agentic AI Engineer who designs production-ready systems and leave with an AI agent portfolio that proves it.

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What Early Course Takers Are Saying

Great value for agentic knowledge

Victor Palomares

This course starts with the basics, giving you a great foundation, and gradually build on more concepts until being able to deliver a enterprise-grade production-ready reliable agentic system. I fully recommend it!

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An excellent course on Agentic Engineering

Luong Nguyen Dinh

This course has helped me a lot in understanding agentic engineering - from core concepts all the way to production deployment. I really love how Paul walk through the entire process of building agents and show how to adapt and improve by learning...

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Great course

Ahmed Medhat

Every AI Engineer needs course like that.

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AMAZING CONTENT

Shadrack Darku

This has trully been an awesome experience full hands-on with clear explanations and real world scenarious. It couldn't have been better. Great thing is videos are going to be added soon to enrich the whole experience. Highlyrecomend this course

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Hands-On, Industry-Centered, Production-Ready Course

Abreham melese Booobnb

The course is industry-focused, emphasizing real-world constraints rather than flashy demos, and it is highly hands-on.

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Amazing Course

Nelson Pullella

Muy buen curso. completo. al grano. se repasan todos los aspectos de los agentes modernos y de sus crecientes capacidades. además se integran con otras herramientas y no queda afuera el deploy, monitoreo y evaluación de los agentes, cosa q es muy ...

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To Agent or Not to Agent?

Most developers get stuck in three places when building agents. We teach you to navigate all three.

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.

What You'll Build

You implement every technique in pure Python: notebooks first for speed, then production code you ship from your own repo.

Notebooks let you isolate, run, and inspect one idea at a time. Every piece moves into the deployed system.

Foundations from Scratch

Learn foundational agent techniques through 11 hands-on builds. You'll implement:

Structured Outputs – Pydantic schemas, JSON extraction, native Gemini integration; build a document metadata extractor
Context Engineering – state management, context selection, compression, caching, and the lost-in-the-middle problem
Workflow Patterns – implement chaining, routing, and parallelization patterns by hand
Function Calling – tool definitions, execution flows, and connecting LLMs to external APIs
Reasoning Strategies – compare Chain-of-Thought, ReAct, and Plan-and-Execute side by side
ReAct Agent from Scratch – build the full observe-think-act loop with zero frameworks
RAG Pipeline – chunking, embeddings, hybrid search, re-ranking, HyDE, GraphRAG, and Agentic RAG
Agent Memory – implement semantic, episodic, and procedural memory with mem0 + ChromaDB
Multimodal Retrieval – extend pipelines to images and document layout
Mini MCP Client – build your first client for the protocol behind both production capstones

An Autonomous Research Agent

Build an autonomous research system to synthesize structured research notes. You'll implement:

Multi-source data collection – crawl websites, GitHub repositories, and YouTube videos
ReAct reasoning loops – iterative research patterns
Tool orchestration – integrate Firecrawl, gitingest, Gemini, and Perplexity via function calling
Multi-round research cycles – automated query generation and refinement
Human-in-the-loop feedback – user-approved query workflows

Deterministic Writing Workflow

Transform research into publication-ready content with multi-modal outputs. You'll implement:

Multi-modal generation – text, Mermaid diagrams, and images
Chain-of-thought prompting – coherent long-form content generation
Evaluator-optimizer patterns – automated quality control loops
Orchestrator-worker architecture – context engineering for style control
MCP-based editing tools – real-time collaboration with Cursor
LangGraph workflows – production pipeline orchestration

Agent Architecture and Production Stack

Master the complete production stack for AI agents, from foundations to deployment. You'll implement:

Durable workflows – LangGraph's functional API for stateful orchestration
Custom evaluation frameworks – LLM judges and alignment scoring
Opik monitoring – debugging and tracing non-deterministic behavior
Production deployment – Docker, GCP, CI/CD pipelines
MCP Authentication – Descope multi-tenant user integration
Integration patterns – Custom agentic functionality on top of Cursor, Claude, or other harnesses

Learn the Discipline that Turns LLM Calls into Reliable Multi-Agent Systems

Explore the First 7 Lessons Free

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Must Have course to learn Agentic AI Engineering

Vinay Gupta

This is one of the best online course available on internet and I am really enjoying learning from this course and putting it into practice. Thanks Towards AI team for this course. Really grateful to you for the support.

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Complicated topic presented very well

Mark Chase

Agentic engineering is a very complicated topic. There are many moving parts in the process of creating an agent. This course not only teaches you these parts, but also how to select which parts you want to use. I liked how they presented not only the reasons why to do something, but also any drawbacks of that option as well. You really get a clear understanding of the entire process.

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Practical Minimum Viable Learning Path

abhijeet choudhari

Theory and practical trade-off covered makes it complete. Diagram and code added to each further improved the clarity. Learning the book and this course should be good enough as foundation for anyone.

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An excellent course on Agentic Engineering

Luong Nguyen Dinh

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A Practical, Production-Grade Guide to Agentic Engineering

Shekhar Tanwar

This course goes beyond theory and focuses on how agentic systems are actually built in the real world, with clear explanations of design trade-offs, project structure, tool selection, context engineering, and evaluation loops. The code and workflows feel industry-standard, making it especially valuable for engineers looking to ship reliable, scalable agentic systems.

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Best Resource on Agentic AI

Hemkumar Poosala

In this fastpaced world where the technology is changing very rapidly I was looking for Agentic AI course with real time projects which are not just notebooks and theory but shipped products so luckily i came to know about the course Agentic AI Engineering by Louis Bouchard and Paul Lusztin i was amazed by the way the course has been designed it has in depth concepts and practical implementations which was wonderful, planning to implement these skills in my job . I throughly enjoyed learning this course and i would recommend anyone who is looking out for Agentic AI course this will help them in their career .Amazing work Louis and Paul kudous to you both.Thanks for giving the wonderful oppurtunity.

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TLDR Foundations to Agent AI Eng

Jose-Alberto Salazar-Jimenez

The course gives the foundations necessary to understand what agent AI eng is about

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Course Curriculum

Part 1: Foundations of Workflows and Agents

Part 2: Agentic Techniques & Building Agentic Systems

Part 3: Evaluation, Observability, and Production Engineering

Part 4: Your Capstone Project

Risk-Free Learning: 30-Day Money-Back Guarantee

If you explore the early part of the course and feel it’s not what you need, you can request a refund within 30 days by emailing us at [email protected]

Start Risk-Free

How It Works

Self-paced project-based learning designed for working professionals.

Self-Paced + Instructor Support: Learn on your schedule with lifetime access. Join dedicated Discord channels 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 and 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

For software engineers, data engineers, data scientists transitioning into AI Engineering

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

Build Your First Agent This Week

󠁯•󠁏 Stop calling LLMs. Start engineering agents.
󠁯•󠁏 Own two agents, fully production-ready
󠁯•󠁏 Gain portfolio-ready skills with certification
󠁯•󠁏 Master fundamentals that outlast trends

Self-paced learning • Regular updates • Risk-free enrollment

Enroll Now

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.

FAQ

Will I get a certificate upon completion?

Yes, you will receive a Towards AI certificate upon completion.
How does it compare to your "Full Stack AI Engineering" course?

The Full Stack AI Engineering course teaches the complete LLM product stack including RAG, fine-tuning, deployment, and agents through one central AI product. The Agents course goes much deeper into designing, orchestrating, evaluating, and deploying production-grade autonomous systems. If Full Stack teaches you how to build an AI product, Agents teaches you how to engineer the autonomy inside it.
What projects will I work on?

You'll build two interconnected production agents from the ground up: a Research Agent and a Writing Workflow, with human-in-the-loop capabilities, while mastering the foundations of agentic architecture.
How is this course different from other AI courses?

Unlike many theoretical courses, this one is fully hands-on, project-based, and focused on the skills top companies are hiring for right now. We guide you through every step, ensuring you not only understand the concepts but can also apply them immediately.
Do you have a referral or affiliate program?

For every referral you provide, you’ll gain access to our exclusive eBook. If you refer three or more people, we’ll send you a physical copy of our book as a thank you! Additionally, we have an affiliate program for individuals with an audience. By joining, you can earn commissions for every successful referral made through your unique affiliate link. Please email Louis-François at [email protected] with proof of referral.
Can I get a discount?

Yes, there are three ways to receive a discount on our courses. If you're a student, we offer a 50% discount on all courses. Additionally, if you've previously purchased our book or another course, you're eligible for a discount. We also provide a bundle discount for groups of two or more people. For further details or to claim your discount, please email Louis-François at [email protected] for a follow-up.
Can I take this course within my company?

Yes! We offer both course bundles and custom training solutions tailored specifically for companies. For more information on company packages or to discuss a customized training plan, reach out to Louis at [email protected].
Our refund policy

If you feel the course isn’t right for you, we’ll provide a 100% refund within 30 days—no hassle, no worries.

Build 'Truly' Autonomous Features for Your Workflows, Company, and Product

From agent user to agent builder

Enroll Now

What You'll Build

You implement every technique in pure Python: notebooks first for speed, then production code you ship from your own repo.

Notebooks let you isolate, run, and inspect one idea at a time. Every piece moves into the deployed system.

Pure Foundations

that go deeper than frameworks

The mechanics behind agents, workflows, and every tool you'll use in production - taught with code.

Architecture & Reasoning - workflows vs. agents, Chain-of-Thought, ReAct, Plan-and-Execute
Structured Outputs & Context Engineering - Pydantic schemas, state management, compression, caching, context isolation
Function Calling - tool schemas, execution flows, building a @tool decorator framework
RAG, Memory & Multimodal - hybrid search, re-ranking, HyDE, GraphRAG, Agentic RAG, persistent memory with mem0 + ChromaDB

Four Mini Projects

ready-to-run notebooks, zero setup

Put every foundation to work - four small builds before production begins.

ReAct Practice - the observe-think-act loop built from scratch, no framework
Workflow Engine - chaining, routing, parallelization, and orchestrator-worker wired together
Multimodal RAG Agent - retrieval and reasoning across text, images, and document layout
MCP Client - your first working client for the protocol behind both production capstones

An Autonomous Research Agent

your first production system

Build an autonomous research system to synthesize structured research notes. You'll implement:

Multi-source data collection – crawl websites, GitHub repositories, and YouTube videos
ReAct reasoning loops – iterative research patterns
Tool orchestration – integrate Firecrawl, gitingest, Gemini, and Perplexity via function calling
Multi-round research cycles – automated query generation and refinement
Human-in-the-loop feedback – user-approved query workflows

Deterministic Writing Workflow

production workflow pipeline

Transform research into publication-ready content with multi-modal outputs. You'll implement:

Multi-modal generation – text, Mermaid diagrams, and images
Chain-of-thought prompting – coherent long-form content generation
Evaluator-optimizer patterns – automated quality control loops
Orchestrator-worker architecture – context engineering for style control
MCP-based editing tools – real-time collaboration with Cursor
LangGraph workflows – production pipeline orchestration

Multi-Agent Integration

connecting both agents into one system

Wire the research agent and writing workflow into a unified, orchestrated system. You'll implement:

Central LLM orchestration - a single LLM selecting tools across both agents dynamically
Multi-server MCP client - one client connecting to multiple independent agent servers
Composed MCP server - a unified endpoint that proxies both agents via FastMCP

Production Stack

ship, monitor, and maintain

Everything that separates a demo from a system you can ship. You'll implement:

Opik observability - tracing and debugging non-deterministic agent behavior
Custom evaluation frameworks - LLM judges, alignment scoring, evaluation datasets
CI/CD for AI - three-tier testing, mocked LLM tests, GitHub Actions, AI evals as regression checks
Authentication - multi-tenant users with Descope
Deployment - Docker, GCP, database integration, file management
Capstone: build your own MCP server - the project that earns certification

Agents Work Locally, Break in Production

Not Sure When Agents Are Worth It

Skills Get Outdated Fast