Build AI agents that get things done
Stop writing prompts. Start shipping autonomous systems. This is a hands-on engineering curriculum for professionals who are ready to design, orchestrate, and deploy goal-driven AI agents in the real world.
My job is to take you from 'I understand agents conceptually' to 'I just shipped one' — and everything in this curriculum is designed with that gap in mind.— Freddy Foster
What you'll learn
What you'll be able to do
- Design and deploy multi-step AI agents that autonomously plan and execute complex tasks end-to-end.
- Build reliable tool-using agents that can browse the web, run code, call APIs, and manage files.
- Architect multi-agent pipelines where specialized agents collaborate, delegate, and self-correct.
- Evaluate and debug agentic systems using structured testing, tracing, and observability techniques.
- Apply memory and context management strategies to give agents persistent, long-horizon capabilities.
- Ship a production-ready agentic application with safety guardrails, cost controls, and human-in-the-loop checkpoints.
How it works
A school that adapts to you
This isn't a set of static videos. Every lesson is generated live and tuned to where you actually are.
We learn your level
A quick placement check tailors your starting point so you're never bored or lost.
Lessons adapt as you go
Each lesson is written for your pace and your goal, adjusting as your skills grow.
Your AI coach keeps you moving
Checkpoints, feedback, and gentle nudges turn progress into a real result.
The curriculum
What's inside your school
6 modules · 26 lessons
Foundations of Agentic AI
Establishes the mental models, vocabulary, and architecture patterns that underpin every autonomous agent system.
- 1.1From Prompts to Agents: A Paradigm ShiftIncluded
- 1.2The Anatomy of an AI AgentIncluded
- 1.3Reasoning Loops: ReAct, Plan-and-Execute, and BeyondIncluded
- 1.4Choosing Your Stack: Frameworks and ModelsIncluded
Tool Use and Action Design
Teaches agents to interact with the outside world by designing, integrating, and securing a rich tool layer.
- 2.1Designing Tools Agents Can Actually UseIncluded
- 2.2Web Browsing and Search IntegrationIncluded
- 2.3Code Execution and SandboxingIncluded
- 2.4API Calls and File ManagementIncluded
- 2.5Tool Safety, Permissions, and Error HandlingIncluded
Memory and Context Management
Gives agents persistent, long-horizon awareness through structured short-term, long-term, and semantic memory systems.
- 3.1Short-Term Context: Managing the Agent's Working MemoryIncluded
- 3.2Long-Term Memory with Vector StoresIncluded
- 3.3Episodic and Semantic Memory PatternsIncluded
- 3.4Memory Retrieval, Forgetting, and Refresh StrategiesIncluded
Multi-Agent Orchestration
Covers how to architect collaborative networks of specialized agents that delegate, communicate, and self-correct.
- 4.1Multi-Agent Architecture PatternsIncluded
- 4.2Building an Orchestrator AgentIncluded
- 4.3Agent-to-Agent Communication and Shared StateIncluded
- 4.4Self-Correction and Reflection LoopsIncluded
Evaluation, Debugging, and Observability
Provides structured techniques for testing, tracing, and continuously improving agentic systems in development and production.
- 5.1Why Standard Testing Breaks for AgentsIncluded
- 5.2Designing Agent Evaluation SuitesIncluded
- 5.3Tracing Agent Runs with Observability ToolsIncluded
- 5.4Debugging Common Failure ModesIncluded
Shipping Production-Ready Agents
Guides learners through deploying a complete, safe, and cost-controlled agentic application ready for real-world use.
- 6.1Safety Guardrails and Content ControlsIncluded
- 6.2Human-in-the-Loop CheckpointsIncluded
- 6.3Cost Management and Latency OptimizationIncluded
- 6.4Deployment Patterns and InfrastructureIncluded
- 6.5Capstone: Build and Ship Your Agentic ApplicationIncluded
Who it's for
Is this you?
Backend developers
You build APIs and services for a living and want to add autonomous, goal-driven agents to your stack with the same engineering rigour you apply to everything else.
ML engineers
You're comfortable with models and pipelines but want to move up the stack into designing the agentic systems that put those models to work in production.
Technical product managers
You need to understand agentic architecture deeply enough to spec it, evaluate it, and lead teams building it — not just describe it in buzzwords.
AI-curious founders
You're building a product with an AI layer and need to understand what's actually possible with autonomous agents before you hire a team to build it.
Power-user knowledge workers
You've maxed out ChatGPT and Copilot and are ready to build custom agents that automate your most complex, multi-step workflows end-to-end.
DevOps & platform engineers
You're the person who'll have to deploy and operate these systems, and you want to understand cost controls, observability, and infrastructure patterns before the agents land in your lap.
Questions
Frequently asked
Your teacher
A note from your teacher
Freddy Foster
If you've been using AI tools seriously for any amount of time, you've probably hit the ceiling.
You've seen what a well-crafted prompt can do, and you've also seen exactly where it breaks down — the moment a task has more than two steps, requires real-world information, needs to call an external system, or has to run while you're not watching it. You patch it with more prompting, add some glue code, and end up with something that works just well enough to be fragile. I know that pattern because I've been there, and I've watched a lot of talented engineers get stuck there.
The shift to agentic AI isn't just a new technique. It's a different way of thinking about what software can be. When you design a system where an agent can plan a sequence of actions, use tools to carry them out, check its own work, call on a specialist sub-agent when it needs to, and hold context across an entire long-horizon task — you're not writing prompts anymore. You're building something that behaves. That shift is what this school is about.
Everything in this curriculum came from the hard questions: How do I give an agent memory that actually works at scale? How do I build a multi-agent pipeline that doesn't fall apart when one agent makes a wrong call? How do I evaluate a system that's non-deterministic by design? How do I ship this to production without it costing a fortune or doing something it shouldn't? Those questions have real, engineering-grade answers — and that's what we work through together, with running code and concrete decisions, not slides and theory.
You will leave this course having built real things: tool-using agents, orchestrated multi-agent pipelines, memory-augmented systems, and a production-ready application with guardrails and observability in place. The goal isn't to understand agentic AI — it's to be someone who ships it.
If you're ready to move from user to builder, this is where you start.
— Freddy Foster
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- 6 modules, 26 lessons
- AI-adaptive lessons tuned to your level
- Quizzes & checkpoints to lock in progress
- Your own AI learning coach
- Learn on any device, at your pace
- Full access for as long as you're subscribed