AI Agents: Building Teams of LLM Agents that Work For You

AI Agents: Building Teams of LLM Agents That Work for You is a practical, hands-on course focused on designing and deploying collaborative teams of Large Language Model (LLM) agents. Instead of building single chatbots, this course teaches how to create multiple AI agents that communicate, coordinate, and solve complex tasks together.

The course emphasizes real-world agent architectures using AutoGen and the ChatGPT API, along with optional deployment and monetization strategies. It’s designed for learners who want to move beyond basic prompting and start building agent-based AI applications that can run locally or scale in the cloud.

Course Snapshot

• Rating: 4.5 / 5

• Students Enrolled: 18,457+

• Skill Level: Beginner → Intermediate

• Language: English (Auto captions), German (Auto captions)

• Content Length: ~9.5 hours

• Format: On-demand video + downloadable resources

• Access: Full lifetime access

• Refund Policy: 30-day money-back guarantee

What This Course Focuses On

This course centers on multi-agent AI systems, teaching how to:

• Build teams of LLM-powered agents

• Enable agents to communicate and collaborate

• Design agent-based workflows for complex tasks

• Turn agent logic into real applications

• Deploy AI agent apps locally or in the cloud

Rather than heavy theory, the course takes a build-first approach, guiding learners through practical implementations using modern agent frameworks.

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Tools & Technologies Covered

Learners gain hands-on experience with tools commonly used in agentic AI development:

• LLM & Agent Frameworks

  • AutoGen

  • ChatGPT API

• Application Development

  • Streamlit (frontend interface)

• Deployment & Scaling (Optional)

  • Google Cloud Platform

• Productization (Optional)

  • Payment system integration for AI apps

Each tool is introduced in context, showing how agents interact, exchange messages, and complete tasks together.

Skills & Concepts You’ll Learn

Multi-Agent AI Systems

• Designing teams of AI agents with defined roles

• Coordinating agent communication and collaboration

• Solving complex tasks using agent interaction

LLM Agent Application Development

• Building LLM-powered applications

• Connecting AI agents to real-world workflows

• Structuring agent-based logic beyond simple prompts

AutoGen & Agent Communication

• Using AutoGen to enable agent-to-agent communication

• Managing conversations and task delegation between agents

• Creating coordinated agent workflows

Frontend & User Interaction (Optional)

• Building a simple frontend to interact with AI agent teams

• Exposing agent systems to end users

Deployment & Monetization (Optional)

• Running AI agent apps at scale using Google Cloud

• Setting up basic payment systems for AI-powered services

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Who This Course Is Best Suited For

• Developers exploring agentic AI systems

• AI engineers interested in multi-agent collaboration

• Builders experimenting with AutoGen and LLM agents

• Entrepreneurs looking to productize AI agent apps

• Learners moving beyond single-chatbot AI solutions

Prerequisites

• Basic familiarity with APIs or programming concepts is helpful

• No advanced AI or ML background required

• Curiosity about agent-based AI systems

Practical Value

What makes this course valuable is its focus on collaborative AI agents, not just standalone models. Learners see how:

• Multiple agents can divide responsibilities

• Agents can reason together to complete tasks

• Agent-based architectures scale better than single prompts

The optional sections on deployment and payments add real-world product perspective.

Career & Use-Case Relevance

Skills from this course are applicable to:

• AI Agent Engineer roles

• Generative AI application development

• Automation and workflow AI systems

• AI-powered SaaS products

• Research and experimentation with agentic AI