Uber

Directi

Morgan Stanley

Gaurav Sen

Founder @AIEngg, Software Engineer

Building production-ready AI Agents for Software Engineers

How Agents work end to end: tooling, context management, and the agentic loop.

Learn what is an AI Agent

Tools, Memory, Guardrails, and Evals: how to improve quality, latency, and cost.

Learn the tradeoffs in Agentic systems

Build an Agent using LLMs, Memory, and Tools, with a real-world use case.

Learn how to implement an AI Agent at the workplace

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

Professor & AI Expert (600K+ 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 2

Cohort 3

⭐"Join the instructor trusted by 600K+ 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.

Learn about the tools, behaviors, systems, & business models powering the AI agent future, and how to steer your products + services to win.

Founder, Free Agency & AI Muscle

OpenAI

NVIDIA

Ramp

Anthropic

Google

Sherveen Mashayekhi

Founder @ Free Agency + AI Muscle. AI product leader, angel investor, supernerd, and coach.

Cohort 1

Hey there. We're at the beginning of the Agent Experience (AX) layer -- products like Claude Code, OpenClaw, Codex, + Manus are showing us early versions of a new business paradigm:Moving forward, your customer is both:<ul><li>your principal: a human at the other end with a goal</li><li>your operator: an AI agent operating on their behalf</li></ul>More and more, AI agents will be recommending products to their users, navigating between options, executing a transaction, &amp; even using your service or product on an ongoing basis.You HAVE to build differently for agents.Unlike humans, they don't buy from emotion-driven landing pages. They don't have eyes that prefer bright colors to draw their attention to next steps. They definitely prefer to avoid your interface if they can avoid it.Companies like Stripe and Salesforce are going agent-first with their releases, startups like AgentMail are building toolkits to help agents become more autonomous and capable, and OpenAI &amp; Anthropic are embracing long-running agents as the killer use case of AI models.Learn how to build your product or service around the agent lane for AI, the new human lane that keeps people in mind, and the control lane that makes it all work.

A new customer is replacing humans: AI agents.

Agent Experience (AX) Foundations

Building the Agent Lane

AI Agent Transactions and Product Use

Business Models of the Agentic Future

The Agent Frameworks of the Future

Tactics and Technicals

You'll walk away: understanding how agents behave (marketers), adopt your product/business (product), or implement with nuance (engineers).

No technical background required, though technical concepts are covered!

Product leaders, designers, marketers, and engineers looking to ensure their team doesn't get beaten by an agent-first competitor.

Founders and CEOs of startups, software companies, and services businesses who want to serve agents as they become the #1 customer.

Builders who are building autonomous AI agents looking to ensure their capability and compatibility in the ecosystem.

Build and reason about enterprise agents grounded in your team's knowledge base

Agentic Search Expert

Shopify.com

Reddit

Wikipedia

LexisNexis

Apple

Doug Turnbull

Led teams at Shopify, Reddit, Wikipedia

AI Builder/Educator (6 Million Students)

Instagram

Netflix

Yale

Hugo Bowne-Anderson

AI & data engineer, consultant, educator of 6+ million students (ex-Yale)

August 2026

Your company sits on endless reports, documents, and files. Agents leverage your company’s rich volumes of data to support customers and make business decisions.In this course agentic search expert Doug Turnbull partners with AI expert Hugo Bowne-Anderson to create agents that actually work.At the foundation of production agents sit an LLM, a reasoning loop, an agent harness, and a layer of context engineering + retrieval.Research consistently shows AI answer quality depends more on the underlying search than any other factor. We’ll go from the ground up - building an agent from scratch, giving you what you need to make them actually useful with accurate search + retrieval.You will leave with a working e-commerce agent, a retrieval pipeline, a starter eval set, and a deployment-ready architecture you can adapt to your own company data.Enroll before July 7 to get very early bird pricing at $750. Early bird pricing increases to $850 on July 7 and the full course price of $950 begins on August 1.

Real agents don’t just chat: they find, organize, and act.


Build agents from first principles

Ground agents in enterprise knowledge

Engineer reliable agent harnesses

Evaluate, automate, and deploy agents

Data scientists, machine learning engineers who are sick &amp; tired of seeing and building prototypes &amp; want to ship reliable LLM applications

Software engineers who want to learn how to build Generative AI systems and learn the LLM software development lifecycle.

Search technologists who connect enterprise knowledge with AI systems with RAG + Agentic Serach

Go from building agent demos to shipping production-grade agent systems with the eval rigor, guardrails, and judgment real teams rely on.

GP, Musa Capital · ex-Microsoft operator

LinkedIn

Productiv

Musa Venture Collective

Allen Smith

General Partner, Musa Capital (ex-Microsoft, NFL, Stanford)

Cohort 1 — Summer 2026

There's a canyon between an agent that works in your notebook and one that survives real users, real edge cases, and real money on the line. You've felt it: the demo lands, everyone's excited — then it breaks the moment it hits production, and nobody quite knows why.Crossing that canyon is the single most valuable skill in AI right now. And almost nobody teaches it, because almost nobody has shipped it.Over six weeks, you'll build a production-grade agent system end to end — architecture, tool orchestration, memory, evaluation harnesses, guardrails, deployment. Not a toy. Your own real project, the kind you can demo to a hiring manager, a CTO, or an investor.What makes this different: you're learning from an active investor and former big-tech operator who builds agentic systems as part of how Musa Capital develops its thesis. Not theory from someone who read the papers — the patterns that separate funded companies from prototypes.You'll leave with a system that ships, an eval discipline most teams never build, and the judgment to know what's actually production-ready.

Go from AI demos that impress to agent systems that survive production

Design agent architectures that survive real-world complexity

Build reliable tool use and orchestration

Implement memory and state for long-horizon tasks

Build eval harnesses that catch failures before users do

Add guardrails, governance, and human-in-the-loop control

Deploy, monitor, and position agentic products

Technical Founders — shipping agentic features and need production patterns that scale beyond the demo.

Engineers &amp; Eng Leads — building with LLMs at companies and need governance, eval, and deployment rigor.

Technical PMs — scoping production-grade agents and need to speak the language of what's actually possible.

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Engineers and engineering leaders who build, deploy, and scale software systems.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Designing scalable, reliable software systems — architecture patterns, distributed systems, and technical tradeoffs.

Operating and deploying large language models in production — fine-tuning, inference, monitoring, and cost optimization.