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AI Agents &
Business Automation
Systems

Build autonomous AI agents, architect multi-agent systems, and deploy full-scale business automation. For builders who want to create AI products, not just use them.

28 modules
~22 hours of content
Pro recommended
Lifetime updates
$749 one-time payment
  • 28 video modules + written guides
  • LangGraph agent starter templates
  • Fine-tuning pipeline (LoRA)
  • End-to-end AI product template
  • Eval framework (RAGAS + custom)
  • Lifetime access + all future updates
  • Priority community access
Buy Now — $749 Questions? Contact us

What you'll learn

This is the builder-level course. You'll go from understanding how agents work conceptually to implementing ReAct, Plan-and-Execute, and multi-agent architectures in LangGraph. You'll deploy real agents that use tools, call APIs, read files, and coordinate with other agents.

You'll also cover fine-tuning with LoRA, building evaluation frameworks, and the operational side of running AI systems in production — monitoring, cost attribution, and governance. The capstone is a full AI product you design, build, and ship.

Who this is for

🚀

Startup founders

Build your AI product from scratch — architecture, implementation, deployment, evaluation.

🧑‍💻

Senior developers

Graduate from using LLM APIs to building production-grade agent systems and pipelines.

🏢

CTOs & tech leads

Understand what's actually possible and how to evaluate, buy, or build AI systems for your org.

🎯

AI consultants

Deliver production AI systems to enterprise clients with a repeatable architecture playbook.

Curriculum — 28 modules

01
Agent architecture patternsReAct, Plan-and-Execute, Reflexion, and multi-agent supervisor patterns — theory and tradeoffs.
02
LangChain from scratchChains, memory, tools, and retrievers. What LangChain is actually good for in 2026.
03
LangGraph — stateful agentsGraphs, nodes, edges, and state. Building agents that can loop, branch, and recover from errors.
04
Tool use & function callingGiving agents access to APIs, databases, file systems, and web search. Tool definition best practices.
05
MCP protocol deep-diveModel Context Protocol — how Claude agents connect to tools and what it means for production.
06
Multi-agent systemsOrchestrator + specialist agent patterns. Routing, handoffs, and inter-agent communication.
07–10
Build a multi-agent research systemFour modules: planner agent, researcher agent, writer agent, and quality reviewer agent working in concert.PROJECT
11
Fine-tuning basics with LoRAWhen to fine-tune vs prompt engineer. LoRA fine-tuning for GPT-5.3 and Llama 4 — practical setup.
12
Llama 4 — local & cloud deploymentRunning Llama 4 locally with Ollama, via Together AI, and hybrid cloud/local architectures.
13
Evaluation frameworksLLM-as-judge, RAGAS for RAG evaluation, custom eval pipelines, and regression testing.
14–16
Production deployment & opsDocker + cloud deployment, tracing with LangSmith, cost attribution, alerting, and incident response.
17–20
AI governance & team workflowsAccess controls, audit logs, ROI tracking, AI policy frameworks, and getting buy-in from stakeholders.
21–28
Capstone: end-to-end AI productEight modules building your own AI product from spec to deployment — architecture, implementation, evaluation, and a presentation-ready demo.CAPSTONE

Tools you'll work with

🦜 LangChain
🕸️ LangGraph
🤖 GPT-5.3
🔮 Claude Opus 4.6
🦙 Llama 4
🌊 Together AI
🦙 Ollama
🔭 LangSmith
📊 RAGAS
🐳 Docker
☁️ Railway / Fly.io