TR
Tuhin Rawat
Product Manager · Walmart
Production-Grade Engineering Course
Building Multi-Agent
AI Products
As a Product Manager, I kept hitting a wall — I could talk about AI systems, but I couldn’t truly understand how they were built. So I did something about it. I used LLMs as my personal guide, asked them to teach me every concept, challenged every answer, and built each piece from scratch myself. This course is the result — a complete, end-to-end documentation of that entire learning journey. Not a textbook. Not a tutorial someone else wrote. Every module, analogy, and line of code came from me prompting, questioning, building, and understanding — one concept at a time.
13
Modules
26
Exercises
171
Code Files
8
Weeks
Python 3.11+
LangChain
LangGraph
AutoGen
CrewAI
Semantic Kernel
FastAPI
Streamlit
Chroma
OpenTelemetry
Pydantic
Tenacity
The Story Behind This Course
Imagine you’re a manager at a busy customer support company. Every day your team gets hundreds of questions — ticket statuses, meeting requests, follow-up emails, billing explanations. Your team is stretched thin. You decide to build an AI assistant to help.
You don’t need to write every line of code yourself — but you do need to understand what you’re building, why each piece exists, and what to expect. That’s exactly what this course teaches. Each module is one step in that journey.
01
Foundations of Agentic AI
You realise your old chatbot just follows a fixed script — it can’t think. You need an assistant that can read a situation, decide what to do, take action, and check its own work. That shift from script to decision-maker is what “Agentic AI” means. Module 1 is your aha moment.
02
Anatomy of an AI Agent
Before you can manage an AI system, you need to know what’s inside it. Module 2 is like opening the hood of a car for the first time — you see the engine (the LLM), the sensors (perception), the gear system (planning), and the wheels (actions). You learn exactly what each part does and why it exists.
03
Multi-Agent Architectures
One agent is a lone worker. But real work needs a whole team with a clear org chart. Module 3 shows you the different ways to structure that team — who reports to whom, who can call whom, and how to avoid everyone talking at once.
04
Frameworks Deep Dive
Just as you’d pick the right tool — Excel vs Notion vs Jira — for different jobs, there are different AI frameworks for different agent tasks. Module 4 is your framework comparison guide: CrewAI, Semantic Kernel, LangGraph — what each is good at and when to use it.
05
First Multi-Agent System
Theory is over — time to build. You assemble your first real team of AI agents working together: a supervisor that delegates to specialists, and specialists that report back. Like running your first stand-up meeting with a team you hired yourself.
06
Tool Use & Action Execution
Your assistant is smart but has no hands — yet. Module 6 gives your AI real tools to use: databases, approval queues, email systems, calendars. Now instead of just answering questions, it can actually do things in your systems.
07
Memory Systems
A customer calls back and your assistant says “Who are you?” — embarrassing. Module 7 gives your AI a full memory system: short-term memory for the current conversation (like a notepad) and long-term vector memory for persistent preferences (like a CRM note that never expires).
08
Communication Protocols
Your agents need to talk to each other reliably — but how? Module 8 defines the language and rules your agents use to pass messages, like deciding that all inter-team memos follow a standard format so nothing gets lost in translation.
09
Orchestration & Scheduling
With multiple agents and tasks running simultaneously, you need a project manager that schedules, retries, and handles failures gracefully. Module 9 is your operations playbook — ensuring work gets done even when things go wrong.
10
Observability & Evaluation
Your support centre is running — but is it working well? Module 10 installs your dashboards, audit logs, and quality scorecards: how long does each request take? Which agent makes the most mistakes? Is the AI actually helpful? You can’t improve what you can’t measure.
11
Safety & Guardrails
What if your AI tries to do something it shouldn’t — share a password, wire money, or delete records? Module 11 adds your compliance and safety layer: policy firewalls that block dangerous actions and a human approval queue for anything that needs a real person to sign off.
12
Production Systems
Everything works on your laptop — now ship it to the world. Module 12 wraps your agents into a real web service (FastAPI), containerises it with Docker so it runs anywhere, and adds a budget cap so it never runs up a surprise API bill.
13
Capstone — The Full Product
Opening day. Everything you’ve built comes together into one working product: a Support Operations Copilot that has a real chat interface, a planner, specialist agents, memory, safety reviews, an API with authentication, structured logging, a full test suite, and a Docker container ready to ship. You built an AI product. End to end.
How to Use This Course
🧠
Product Managers
Finally understand what engineers are building. Read the Concept & Tips tabs in each module — no coding required.
👩💻
Developers
Go straight to the Exercises tab and build every file from scratch. Use the Code Explanation tab for Module 13.
🔍
Curious Learners
Start at Module 1 and work forward. Each module builds on the last. Don't skip the analogies — they're the key.
▶ How to Navigate
1
Pick a module from the sidebar
All 13 modules are listed on the left. Each builds on the previous. Start at Module 01 if you're new.
All 13 modules are listed on the left. Each builds on the previous. Start at Module 01 if you're new.
2
Read the Concept tab first
Every module opens on the Concept tab — the core idea, mental model, real-world analogy, and production insight.
Every module opens on the Concept tab — the core idea, mental model, real-world analogy, and production insight.
3
Go to Exercises to build
The Exercises tab has step-by-step coding exercises with all the files. Download the code bundle above to follow along.
The Exercises tab has step-by-step coding exercises with all the files. Download the code bundle above to follow along.
4
Check Tips & Pitfalls
The third tab has pro tips and common mistakes gathered from building these projects. Read it before you get stuck.
The third tab has pro tips and common mistakes gathered from building these projects. Read it before you get stuck.
5
Module 13 is the Capstone
The final module has two extra tabs — About Project (what you're building) and Codebase Explanation (every file explained line by line).
The final module has two extra tabs — About Project (what you're building) and Codebase Explanation (every file explained line by line).
▶ What You Will Learn
✓How LLM agents reason, plan, and use tools
✓How to build multi-agent pipelines with LangGraph
✓How memory (short-term + vector) works in AI systems
✓How to build and expose a FastAPI backend
✓How to design critic and guardrail agents for safety
✓How RAG, ChromaDB and embeddings enable semantic memory
✓How to containerize and deploy with Docker Compose
✓How to add auth, rate limiting, logging, and testing
✓How to ship a full Streamlit UI on top of an AI backend
✓How frameworks like AutoGen, CrewAI, and SK compare
All Modules
Learning Philosophy
◈ Core Principle
Prompts can improve local behavior. Architecture determines system behavior.
This is not a prompt-collection course — it is an engineering course. Every module forces you to ask: when should you add a better prompt? And when should you change the architecture?
Capstone Product
◆ Support Operations Copilot
A complete multi-agent product featuring:
- Chat interface + planner agent + executor agents (CRM, scheduling, email)
- Critic agent for quality review before final response
- Short-term session memory and long-term vector preference memory
- Logging, FastAPI deployment, Streamlit UI, and Docker compose