Ship autonomous agents that run your business
A systems-level curriculum for engineers and ML practitioners who are done with toy demos — and ready to architect, harden, and deploy agents that own entire department workflows end-to-end, in production, without a human babysitter.
An agent you can't decompose is an agent you can't trust — so we build everything from first principles, layer by layer, until you understand exactly what's happening and exactly what to do when it isn't.— Freddy Foster
What you'll learn
What you'll be able to do
- Design a multi-layer agent architecture (perception, memory, planning, action) from first principles and map it to a real department workflow.
- Build and fine-tune a domain-specific LLM-backed agent using tool-use, retrieval-augmented generation, and structured output schemas.
- Implement robust memory systems — short-term context windows, long-term vector stores, and episodic logs — so agents retain and apply institutional knowledge.
- Wire agents into live business systems (CRMs, ticketing platforms, email, Slack) via secure API integrations and event-driven triggers.
- Apply evaluation frameworks and guardrail layers to measure agent accuracy, prevent hallucinations, and enforce policy compliance before production deployment.
- Deploy, monitor, and iterate on autonomous agents in cloud infrastructure with observability dashboards, failure-recovery loops, and cost-control strategies.
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 · 27 lessons
Foundations of Autonomous Agent Architecture
Establishes the core conceptual and technical framework — perception, memory, planning, and action — that underpins every agent built in the course.
- 1.1The Anatomy of an Autonomous AgentIncluded
- 1.2Agent Patterns: Reactive, Deliberative, and HybridIncluded
- 1.3Mapping Agent Architecture to a Business DepartmentIncluded
- 1.4Tooling Landscape and Environment SetupIncluded
Building the Agent Core: LLMs, Tools, and Structured Reasoning
Teaches engineers to construct the reasoning engine of an agent using LLMs with tool-use, function-calling, and structured output schemas.
- 2.1Selecting and Configuring a Domain-Specific LLMIncluded
- 2.2Tool-Use and Function-Calling PatternsIncluded
- 2.3Structured Output Schemas and Prompt EngineeringIncluded
- 2.4Fine-Tuning and Domain AdaptationIncluded
- 2.5ReAct and Chain-of-Thought Planning LoopsIncluded
Memory Systems: Short-Term, Long-Term, and Episodic
Implements all three tiers of agent memory so agents retain context, institutional knowledge, and past interaction history across sessions.
- 3.1Context Windows and Short-Term Memory ManagementIncluded
- 3.2Retrieval-Augmented Generation with Vector StoresIncluded
- 3.3Episodic Logs and Institutional Knowledge CaptureIncluded
- 3.4Memory Retrieval Strategies and Relevance RankingIncluded
Integrating Agents into Live Business Systems
Wires fully reasoned agents into production business tools — CRMs, ticketing systems, email, and Slack — using secure APIs and event-driven triggers.
- 4.1Secure API Integration FundamentalsIncluded
- 4.2Connecting to CRMs and Ticketing PlatformsIncluded
- 4.3Email and Slack as Agent I/O ChannelsIncluded
- 4.4Event-Driven Triggers and Workflow OrchestrationIncluded
- 4.5Multi-Agent Handoffs and Department RoutingIncluded
Evaluation, Guardrails, and Policy Compliance
Applies rigorous evaluation frameworks, hallucination-prevention layers, and policy guardrails to validate agent behavior before any production deployment.
- 5.1Designing an Agent Evaluation FrameworkIncluded
- 5.2Hallucination Detection and MitigationIncluded
- 5.3Guardrail Layers: Content, Action, and Scope ControlsIncluded
- 5.4Human-in-the-Loop Escalation PoliciesIncluded
Production Deployment, Observability, and Iteration
Deploys agents to cloud infrastructure with full observability, automated failure recovery, and cost-control strategies for long-running autonomous operation.
- 6.1Cloud Deployment Architectures for AgentsIncluded
- 6.2Observability Dashboards and Trace LoggingIncluded
- 6.3Failure Recovery and Self-Healing LoopsIncluded
- 6.4Cost Monitoring and Token Budget ManagementIncluded
- 6.5Continuous Improvement: Retraining and Version ManagementIncluded
Who it's for
Is this you?
Senior Backend Engineers
You know distributed systems cold — this lab maps agent architecture to the systems-design vocabulary you already think in, so you can apply it immediately.
ML Practitioners
You've trained models but want to take ownership of the full agent loop — fine-tuning, RAG pipelines, evaluation frameworks, and production deployment included.
Technical Product Leads
You need to spec, oversee, and pressure-test agent builds — this curriculum gives you the architectural fluency to do that without outsourcing your judgment to your engineers.
Platform & Infra Engineers
Observability, failure recovery, cost control, and cloud deployment architecture are first-class citizens here — not footnotes bolted on after the 'real' ML content.
AI/Automation Consultants
You're implementing AI solutions for clients across industries — the integration and guardrail sections give you a defensible, repeatable production playbook.
Founding Engineers at AI Startups
You're building the agent layer that your product will live or die on — this lab covers the full stack from LLM core to business system wiring to the controls that let you sleep at night.
Questions
Frequently asked
Your teacher
A note from your teacher
Freddy Foster
If you're here, you've probably already built something that calls an LLM and does something vaguely useful. Maybe it impressed people in a demo. Maybe it even runs in production — or something you're nervously calling production. And you've noticed the gap: between the demo that works on rehearsed inputs and the system that can own a customer support queue on a Monday morning when half the edge cases are things you never imagined.
That gap is an engineering problem. And it's exactly what this lab is designed to close.
I built this curriculum because the resources I wished existed when I started shipping agents didn't exist. Most content either stops at "here's how to call the OpenAI API" or jumps straight to academic literature that assumes you have three researchers and a cluster. There's very little in between for the working engineer who has real deadlines, a real business system to integrate with, and a real obligation not to let the agent do something catastrophically wrong at 2 AM without a human in the loop.
So that's what we build here. We start from the four-layer architecture — perception, memory, planning, action — not because the taxonomy is sacred, but because designing a system you can't decompose is a system you can't debug. We go deep on memory: context windows, vector stores, episodic logs — because an agent with no institutional memory is just an expensive autocomplete. We wire into live systems — CRMs, Slack, ticketing platforms — because that's where the work actually lives. And we close with the stuff that determines whether you can stake your engineering reputation on this: evaluation frameworks, guardrails, observability, failure recovery, and cost control.
I'm not going to sell you on how transformative this will be for your career. You already know what it means to ship something that actually works at scale. What I'm offering is the precise, unabridged, trade-off-honest engineering path to get there for autonomous agents. If that's the course you've been looking for, welcome to the lab. Let's get to work.
— Freddy Foster
Start your journey today
Join get instant access — learn at your own pace with an AI coach in your corner.
$97/mo
Recurring billing · cancel anytime
Secure checkout · Instant access
- 6 modules, 27 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