Tata Consultancy Services

Wipro

Serco Group

Dheeraj Saxena

Ex-IBM, TCS, Wipro. Executive Architect | 25+ Years Scaling Customer Data, AI & MarTech Solutions

The Role of Architecture Principles in Agentic AI

Understand how principles replace developer-dependent "heroics" with scalable, repeatable enterprise standards.

Understand the Necessity of AI Architecture Principles

Review 15 real-world principles like separate evaluation, deterministic routing, state management and more.

15 Examples Of Principles in Agentic AI

Learn to translate high-level principles into enforceable standards for consistency and auditability.

Standards & Governance

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

Cohort 2

Professor & AI Expert (540K+ Students)

Microsoft

General Motors

Samsung

HSBC

Dr. Ryan Ahmed

Professor, AI expert, & YouTuber who has taught 540,000+ learners worldwide.

Director of Engineering @Launchpad.AI

Nike

Levi's

Mr. Cooper

Launchpad

Kukesh Kodess

Director of Engineering with 7+ years building cutting-edge AI/ML systems

Cohort 1

⭐"Join the instructor trusted by 540K+ students with 40K+ five-star reviews and 50M+ minutes of AI training watched worldwide."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 API wrappers, and they lack the depth to manage state, memory, &amp; multi-agent coordination in real production systems.The Solution: This bootcamp is your bridge to elite Agentic AI engineering. We move beyond the basics to architect robust agent fleets in which 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>Enterprise Reliability: Design for guardrails, persistent memory, observability, &amp; human-in-the-loop safety. </li></ul><h3>Real-world Capstone Project</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.

This is a project-based course. In 6 weeks, you will learn a repeatable framework and build your multi-agent AI application ready to launch.

Cohort 4

Cognizant BR Turma 1

Pearson SME, O'Reilly teacher, ex-Cisco

Cisco

Claro Brasil

Pearson

O'Reilly

Carmelo Iaria

Agentic Architect, Pearson SME, Startup Founder, 30 years of global experience

Cohort 3

Cohort 5

What alumni say<ul><li>Matt (Founder, SAVANT-AI): "Five stars for course logic, course structure, and course value."</li><li>Alexa (Lead UX Designer, Caseware): "I can already see how to bring it into my work."</li><li>Satyan (Group PM, AT&amp;T): "The concepts taught here make the difference between building a product that works and one that doesn't."</li></ul>Why this course mattersIn 2026, engineering value is shifting from task execution to orchestration and systems thinking. Anthropic’s research shows that being a software engineer increasingly means orchestrating agents that write code, evaluating their output, and ensuring the system solves the right problems—not just writing more code yourself. The gap between early adopters and everyone else is widening fast.You will leave with a production-ready capstone architecture, reusable templates, and a delivery playbook you can apply immediately.NOTE: <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/carmelo-iaria/agentic-architect?promoCode=EARLYBIRD">Promo Code "EARLYBIRD" 20% discount expires on May 17th</a>NOTE: Those enrolled to this course can join my <a target="_blank" rel="noopener noreferrer nofollow" href="https://maven.com/carmelo-iaria/agentic-architect-workshop">Workshop </a>for free

Software development is moving from writing code to orchestrating agents.

Define a solid foundation with clear research and project scope

Architect your complete multiagent solution — from roles to workflows

Design and build a production-ready Agentic AI Application

Define and configure your multiagent crew — from backend logic to integrations

Add enterprise-level quality: integrate human feedback, observability, and QA

Deploy, validate, and present your final Capstone solution with confidence

Own delivery outcomes in tech/product/design/consulting. Read-level Python is helpful; pair with a technical teammate if needed.

Experience & Background

Comfort with Git/GitHub, API basics, and AWS (or similar). If rusty, budget extra setup time in Weeks 1-2.

APIs & Cloud Platforms

Agentic IDE (Cursor, Claude Code, GitHub Copilot), OpenAI API credits (~$20+), GitHub account, AWS access (recommended)

Required Tools & Access

Technical Builders (software, AI/ML, data, platform) moving from demos to reliable delivery..

Product and Business Owners (PMs, founders, consultants) responsible for outcomes, priorities, and execution quality.

UX and Experience Leaders (UX, service design, research) shaping human-agent workflows, trust, and adoption.

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

AI Engineer with 10+ years of experience

Hugging Face

DataRobot

Contextual AI

Snowflake

Snorkel AI

Rajiv Shah

Agentic AI Engineer at OpenHands

Winter/Spring 2026

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 1 – June 26 🚨Use Code: Earlybird (20% discount if you sign up by May 15th)---What you get:🔄 GOATS, a 5-step framework you'll use to think through every AI project🛠️ Bring your own AI Agent project and apply the frameworks each week (or use a provided case study)🧑‍🏫 2 Live sessions per week plus office hours📊 Access to 200+ case studies, the largest collection of AI reframing examples📋 Additional reference material covers traditional ML and Gen AI use cases🎥 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 problem 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.Student or between jobs? Message me. Financial assistance is available

Evals aren't enough, Learn the power of reframing AI problems

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. Its about frameworks for thinking, not tutorials.

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

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

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

AI is reshaping software engineering, including architecting. Stay ahead.

Head of Enterprise Architecture

NatWest Group

Boston Consulting Group (BCG)

TIBCO

Vincenzo Marchese

Vincenzo is a practitioner with over 25 years of experience in global businesses

As AI is reshaping software engineering across every phase of the development lifecycle, the ability to architect high quality software is increasingly becoming a differentiating skill.In this hands-on workshop you will learn how to leverage AI as the your Co-Architect for better architectures and increased design velocity.This is NOT a workshop on how to architect AI Systems.The workshop will teach you how to effectively use AI to architect Systems and Solutions.Whether for generating diagrams, drafting Architecture Decisions, or communicating the architecture to your key stakeholders, AI can help.It's not a matter of if. It's a matter of when, to what extent, and with what impact to the role of the architect.You can choose to remain in the lead of architecting and design decisions, or be subject to AI.

Software Engineers and Technical Leads aspiring at taking a more direct approach to architecting

Software, Domain, Solution, and Enterprise Architects