AI Agent Architecture Course

Deep Expertise Track

12 lessons. From a single LLM call to multi-agent orchestration. Every lesson includes runnable Python code, ASCII diagrams, and the exact patterns behind my production apps.

Why I Built This

I spent months studying how AI agents actually work — reading the ReAct paper, Anthropic's "Building Effective Agents", Microsoft's orchestration patterns, and building 3 production agent apps. Most tutorials either stay at "what is an LLM" or jump straight to "use this framework." Neither helps you understand what's actually happening under the hood.

This track is what I wish I had: a structured path from "I know how to call an LLM" to "I can architect a multi-agent system and explain every decision." Every concept comes with ASCII diagrams, runnable code, and real examples from apps I shipped to Hugging Face Spaces.

What You'll Be Able To Do

  • Build a ReAct agent from scratch with zero frameworks (50 lines of Python)
  • Explain the difference between workflows and agents (the #1 interview question)
  • Implement all 5 multi-agent orchestration patterns with working code
  • Design tools that agents can actually use (Agent-Computer Interface)
  • Evaluate agent quality with LLM-as-judge and trace analysis
  • Ship agents to production with cost, latency, and security guardrails
  • Speak confidently about agent architecture in interviews — with real code to show

Prerequisites: You should know Python, have called an LLM API before, and understand basic prompt engineering. That's it. No ML degree, no framework experience required.

Module 1

Foundations

01

The Agent Spectrum

5 levels of AI systems — from direct model call to multi-agent orchestration. Where do your apps sit?
02

The ReAct Loop

Build an agent from scratch with zero frameworks. The Think-Act-Observe loop in 50 lines of Python.
03

LangChain Agents

What LangChain does for you vs what you now know how to do yourself. The abstraction tax explained.
Module 2

Tools & Orchestration

04

Tool Design for Agents

Agent-Computer Interface design, poka-yoke, and why tool design matters more than model choice.
05

Sequential Orchestration

Multi-agent Pattern 1: chaining agents in a fixed pipeline. The pattern behind BA Assistant.
06

Concurrent Orchestration

Multi-agent Pattern 2: fan-out/fan-in. Multiple agents work in parallel, results aggregated.
07

Handoff Orchestration

Multi-agent Pattern 3: agents hand control to each other dynamically. Like a hospital triage.
Module 3

Advanced Patterns

08

Group Chat Orchestration

Multi-agent Pattern 4: agents collaborate through shared conversation. Debate and maker-checker.
09

Magentic Orchestration

Multi-agent Pattern 5: plan, build, execute. The most advanced pattern, for complex projects.
10

Agent Evaluation

LLM-as-Judge, trace analysis, failure mode classification. How to measure agent quality.
Module 4

Production

11

Production Considerations

Cost, latency, state, security. What changes when you move agents from prototype to production.
12

Capstone Project

Combine everything into one multi-agent BA system. From spec to working code.

Sources & References

This track synthesizes material from:

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