Day 7: Introduction to Multi-Agent Systems

Welcome to Lesson #7!

👋 HEY there! I owe you a quick apology for the pause in our 14-Day AI Product Course! A rough virus threw me off track. BUT I’m back and more excited than ever to continue our 14-Day AI Product Engineer course.

📋 Today, you will learn about multi-agent systems (MAS) :

• Limits of single agents

• What are Multi-Agent systems?

• When to use them?

• 2 main patterns with real-world use cases

• Example project with “Sales Multi-Agent system”

• Announcement for the next free “Vibe Coding webinar”

You can find previous lessons here:

👉 Day 1: Intro to Agents
👉 Day 2: Agents vs Workflows 
👉 Day 3: RAG and Tool Use 
👉 Day 4: Memory 
👉 Day 5: Evals for Agents 
👉 Day 6: Context for Agents 

🚨PSA!

The start date of the bootcamp has been moved to June 12th, 2025 from June 2nd, 2025 due to unforeseen health circumstances! Thank you for your patience!

To make it up to you, we are hosting a Free “Vibe Coding Fundamentals”
 webinar on May 31st, Sat, at 12 PST. It’s a 90-minute Live Session, where you’ll learn how to build Agents like a real AI engineer using tools like Cursor, etc!

Don’t miss it! Sign up: HERE!

📌 Find more details on the next bootcamp: Here 

🧐 Hmm… Why are single agents not enough?

Here’s how we used to talk about agentic systems back in 2023 and 2024:

🚫 Why it doesn’t work anymore?

In previous lessons, we learned how to build a single agent with RAG, tools, and memory. But, what if you are building a real, complex, and custom workflow?

If that’s the case, a single agent can often become a bottleneck.

🧐 Limitations of single agents

🔒 A single agent is like hiring one person to run an entire company.

• They can only handle one domain or task well

• They lose track of logic as tasks get complex

• Too many instructions and context in one system prompt.

• They are meant for simple workflows only

• They are hard to debug
  

If you want a real team running your workflows, you need multi-agents. And today, we’re breaking it aaaalll down.

🤖 So, what are multi-agent systems (MAS)?

A multi-agent system (MAS) comprises multiple autonomous agents, each specialized in specific functions, collaborating to achieve complex objectives. 

This architecture is good for:

• parallel processing

• continuous improvement through feedback loops

• tasks specialization and delegation

• Improved fault tolerance

• flexible architecture (add or remove agents)
  

In summary, multi-agent systems are better for complex, long-running tasks that require diverse expertise, parallelism, and fault tolerance.

P.S. Check out my real-world multi-agent “Company Research” tutorial below for a practical example:

🔄 Main Design Patterns of MAS

There are 2 main design patterns with multi-agent systems: 1)Heirchical and 2)Decentralized. See real-world examples for both.

1. Hierarchical (Manager-Worker) 🧠

A central agent assigns tasks to specialized sub-agents. Common in project management and customer service applications.

Structure:

• A manager agent receives the user’s query and determines the intent.

• It routes the query to one of several specialized worker agents, such as:

  • OrderStatusAgent – retrieves order updates

  • ReturnsAgent – handles return requests

  • FAQAgent – answers general policy questions

Why it works:

• The manager handles task delegation, keeping logic centralized.

• Worker agents focus on their specific domain, improving response quality and isolating bugs.

2. Decentralized (Custom) Pattern 🌐

Structure:

• Each agent independently monitors a data source or domain:

  • RedditTrendsAgent tracks subreddits

  • NewsSentimentAgent monitors media outlets

  • StockMovementAgent pulls financial data

• Agents broadcast findings to each other in real time and self-organize to draw conclusions:

  • If Reddit buzz increases and sentiment spikes, RedditTrendsAgent informs StockMovementAgent to watch for related stock moves.

Why it works:

• No central authority needed—agents coordinate dynamically.

• Perfect for fast-changing environments with no single point of control.

📌 When to Use Them?

Multi-agent systems are ideal when:

• Tasks are complex, long-running, or span multiple domains.

• Specialized expertise is required for different components of a task.

• The system needs to adapt dynamically to changing environments.

Check out the table below on when to use single-agent vs multi-agent architecture:

Watch Hai’s Multi-Agent tutorial for Sales Reps 👇

By the end of this tutorial, you will learn how to build a multi-agent system using OpenAI Agents SDK! See the tech stack:

• Agent framework: OpenAI Agents SDK

• Setup: LinkedIn scraping with Scraper API:

• Multi-agent workflow (leader, SDR, emailer)

• Automated personalized email generation

🤯⚡️ If you want to watch a Senior Engineer build full-stack AI applications right in front of you (with all sessions recorded), check out our schedule for June!

And join our Daily Vibe Coding sessions with HAI!

Congratulations on completing Day 7!

👏 You’ve just leveled up your agents with Multi-Agent Systems (MAS)

Tomorrow, we are starting the 2nd half of AI Product Engineer course, which will be all about SWE fundamentals.

🚨 PSA: Updated Bootcamp Start Date is June 12, 2025!

We’ve moved the start date to June 12 due to unexpected circumstances.

To make it up to you, we are hosting a free “Vibe Coding Fundamentals”
 webinar on May 31st, Sat, at 12 PST. It’s a 90-minute Live Session, where you’ll learn how to build Agents like a real AI engineer using tools like Cursor, etc!

Sign up HERE!

⏳ Seats are filling fast.

Use an Early Access Coupon Code “EARLY” for $210 OFF before it expires on May 30

If you have any additional comments, suggestions, or feedback, respond to this email directly. We’d love to hear from you!