Agentic AI

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Everyone

Operators

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Project Managers

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Lawyers

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Investors

Real Estate

Become an Agentic AI Engineer by building real-world, production-ready AI agents that autonomously reason and execute complex tasks.

Cohort 1

Professor & AI Expert (510K+ Students)

General Motors

Samsung

HSBC

Barclays

Stellantis

Dr. Ryan Ahmed

Professor, AI expert, & YouTuber who has taught 500,000+ learners worldwide.

Cohort 2

Agentic AI Engineering is 2026's most in-demand skill. Software is shifting from static systems to AI-native architectures, where autonomous agents reason, plan, and execute complex work.The problem: Most developers are stuck building fragile API wrappers and they lack the architectural depth to manage state, memory, and multi-agent coordination in real production systems.The Solution: This bootcamp is your bridge to elite Agentic AI engineering. We move past the basics to architect robust, fault-tolerant agent fleets where specialized agents collaborate autonomously in production.What makes this course unique?<ul><li>Production-Grade Architecture: Engineer real systems using LangGraph, CrewAI, &amp; AutoGen.</li><li>Automate Agent Workflow Creation: Use Claude Code &amp; Codex to generate, refactor, and evolve agent workflows.</li><li>Standardization (MCP): Build secure, interoperable tools using the Model Context Protocol.</li><li>Enterprise Reliability: Design for guardrails, persistent memory, observability, &amp; human-in-the-loop safety. </li></ul><h3>Capstone Project (Real-World Application)</h3>Design &amp; deploy a production-grade, portfolio-ready agentic AI system for real-world use, demonstrating senior-level Agentic AI engineering skills.

Master Agentic AI Engineering & Architect Production-Grade Autonomous Systems

Architect, build & deploy production-grade agentic AI systems end-to-end

Orchestrate Autonomous Multi-Agent Teams

Master Tool Use & The Model Context Protocol (MCP)

Automate Workflows with AI Coding Assistants

Engineer Context-Aware Agents with Retrieval Augmented Generation (RAG)

Deploy, Monitor & Benchmark for Enterprise

Software Engineers &amp; Developers wanting to master state-of-the-art Agentic AI frameworks and build autonomous, production-grade AI agents.

Technical Founders &amp; CTOs who want to architect scalable, fault-tolerant autonomous systems that serve as your product's core engine.

Modern ML &amp; Data Science Practitioners who want to evolve from static models to dynamic Agentic AI workflows that reason, plan, &amp; execute.

How to think through AI problems end-to-end: scoping, debugging, and knowing when to pivot.

ML Engineer with 10+ years of experience

Hugging Face

DataRobot

Contextual AI

Snowflake

Snorkel AI

Rajiv Shah

Enterprise AI veteran: Shipping AI to production, with clarity and humor

Pilot Cohort

Summer 2026

AI Problem Framing is to AI teams what System Design is to software engineers and Product Sense is to PMs. Whether you're building, evaluating, or leading AI work, this is the foundational thinking skill that separates results from rework.--🚨 Summer cohort: June 2 – June 26 🚨Winter Cohort is finishedUse Code: Earlybird for 20% discount if you sign up by April 30th---What you get:🔄 The Loop, a 5-step framework you'll use to think through every AI project🧑‍🏫 Live sessions and office hours where you can bring your real problems📊 Access to 200+ case studies, the largest collection of AI reframing examples📋 Production-ready checklists for RAG, Forecasting, GenAI🎥 Lifetime access to all recordings---This course is for you if:• Your AI works in demos but fails in production• You've spent months on a model only to realize you solved the wrong problem• You lead AI work but came from engineering, product, or PMYou'll learn from 200+ AI failures in 4 weeks. You gain years of experience and recognize the scars.If you are a student or between jobs, send me a note about your current situation and why this course is important to you, and I may discount the course appropriately.

Most AI fails from bad framing, not bad models. Learn to fix that.

Frame AI problems right the first time

Diagnose what's actually broken

Own the problem, not just the solution

Steer your team past expensive mistakes

We focus on framing and strategy, not explaining what models or training means. Basic vocabulary lets us go deeper faster.

You understand what AI/ML is at a conceptual level

Real experience gives you context. The frameworks click when you can map them to projects you've actually built or managed.

You've worked on or led AI projects in some capacity

This is about decision-making, not implementation. You'll learn what to build and why, not how to code it.

No coding during the course; frameworks for thinking, not tutorials

Engineers who can build AI but want to master outcome engineering: knowing what to build, not just how

Building RAG, agents, or ML models? Let's get your demos into production! Learn the thinking that makes the difference.

Managers who need to evaluate AI/ML proposals, set realistic milestones, and know when to escalate

Learn to design and evaluate a production-ready media recommender: metrics, architecture, cold start, and drafting an effective roadmap.

Principal ML Engineer @ Disney, ex-Meta

The New York Times

The Walt Disney Company

ESPN

Columbia University

Katerina Zanos

Principal ML Engineer @ Disney, x-Meta, x-NYT

Build a production-ready Recommender System Blueprint for a media surface of your choice. This will be an artifact you can take back to work to guide implementation, align stakeholders, and make smarter roadmap calls.Most teams working on recommender systems don’t struggle because they lack algorithms. They struggle because they lack a practical, end-to-end playbook for shipping.You might know the building blocks (embeddings, retrieval, ranking, bandits), but still feel stuck on the hard parts:<ul><li>which metrics actually reflect what product wants</li><li>how to architect the system from logging to serving</li><li>what model to choose under real constraints</li><li>how to handle cold start and sparse data</li><li>and how to prove ML is worth it over heuristics</li></ul>I’ve built recommendation systems in production across news, feeds, and sports over the last 10 years (NYT, Meta, ESPN). This course turns that experience into a clear, reusable process you can apply to your own product without needing FAANG-scale infrastructure. By the end, you’ll be able to confidently design and evaluate production-ready recommenders, defend trade-offs, and use your Blueprint as a durable reference for future iterations.

Design and evaluate a media recommender system that ships to production.

Build a real-world mental model of recommenders (not just algorithms)

Design metrics and success criteria your team will actually align on

Architect the system: make the right calls on retrieval, ranking, and serving

Handle cold start with pragmatic strategies

Know when ML beats heuristics and how to prove it

You are able to interpret (not compute) common product metrics like retention/engagement and discuss tradeoffs.

Metric + product reasoning at a basic level

You can follow an architecture diagram and understand concepts like services/APIs, latency, reliability constraints.

Ability to follow system-level thinking

We will be working with specs, diagrams, and decision frameworks (not code).

Comfort making structured decisions from imperfect data

Senior ML Engineers &amp; Data Scientists who can build models but want sharper judgment on what to build next and how to defend it

Software Engineers transitioning into ML who want to think in systems + measurement, not just training code.

Tech Leads who need an end-to-end playbook to review designs, align metrics with product, and avoid expensive detours.

Diagnose, fix, and prevent the distributed systems failures that every AI coding agent creates - in any language.

Java Champion/OSS/Microservices expert

Broadcom Inc.

VMware

SpringCentral

Marcin Grzejszczak

Java Champion · Creator of Spring Cloud Sleuth & Spring Cloud Contract

AI is rewriting how backend teams build services. The developers who thrive won't be the ones who generate code fastest - they'll be the ones who catch what AI gets wrong.Every AI coding agent - Copilot, Cursor, Claude Code -makes the same distributed systems mistakes. Hardcoded service URLs. No circuit breakers. Missing trace propagation. No timeouts.The gap between "it compiles" and "it survives production" is growing, because AI makes bad code look professional. And no tutorial or YouTube video puts you in front of actual broken infrastructure to practice.This workshop does. You'll generate real multi-service systems with AI tools, then debug them against real failures - latency, flaky services, cascading outages. Any language, any framework.You'll diagnose issues, build missing pieces and write AI instruction files that encode your architecture standards. You'll leave with a production-readiness checklist, a battle-tested rules file, and the instinct to spot what AI misses - before your users do.<a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/forms/e8d298">Please fill out the survey</a> and help me out to prepare the perfect syllabus!IMPORTANT: Course syllabus still can change depending on the survey results

AI generates code fast. It generates distributed systems failures even faster.

Diagnose cascading failures across service boundaries

Build resilience patterns that survive chaos

Instrument full observability across distributed services

Write AI rules files that enforce your architecture standards

Validate AI-generated code with contracts and chaos scenarios

Debug distributed systems confidently in any language or stack

Mid-level backend developer adopting AI coding tools who wants to know what to trust and what to fix before shipping to production.

Senior engineer or tech lead responsible for system reliability whose team already generates code with AI but ships gaps.

Developer working across multiple languages and stacks who wants distributed systems skills that transcend any single framework.

System Design

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