Description

Introduction

As AI systems grow in complexity, collaboration between multiple intelligent agents—each with specialized capabilities—has become a vital paradigm. Multi-agent systems (MAS) leverage distributed intelligence to solve tasks that single agents cannot handle alone. This course explores the theory and practice of multi-agent collaboration within the context of Agentic AI, emphasizing communication, coordination, negotiation, and delegation using LLM-based agents.

Prerequisites

This course is designed for learners who have:

• A foundational understanding of AI and agentic systems

• Experience with Python programming

• Familiarity with LLM APIs (e.g., OpenAI, Claude, Cohere)

• Prior exposure to LangChain, AutoGen, CrewAI, or similar frameworks is helpful but not required

Table of Contents

1. Introduction to Multi-Agent Systems (MAS)

• 1.1 What Are Multi-Agent Systems?

• 1.2 Types of Agents: Cooperative, Competitive, Hybrid

• 1.3 Use Cases: Research, Workflow Automation, Robotics, Simulation

2. Foundations of Agent Collaboration

• 2.1 Communication Protocols Between Agents

• 2.2 Roles, Specialization, and Task Delegation

• 2.3 Planning with Shared and Conflicting Goals

3. Multi-Agent Architectures

• 3.1 Centralized vs Decentralized Control

• 3.2 Blackboards, Message Passing, and Shared Memory

• 3.3 Organizational Structures: Hierarchies, Swarms, Teams

4. Implementing Collaborative LLM Agents

• 4.1 LangChain Multi-Agent Tool Use

• 4.2 OpenAI’s Assistant API with Parallel Threads

• 4.3 Using AutoGen for Agent Conversations

• 4.4 CrewAI: Role-based Multi-Agent Pipelines

5. Advanced Collaboration Techniques

• 5.1 Negotiation and Conflict Resolution Among Agents

• 5.2 Dynamic Task Reassignment and Failure Recovery

• 5.3 Multi-Agent Planning with Memory and History

6. Hands-on Projects

• 6.1 Research Team Simulation: Lead + Analyst + Summarizer Agents

• 6.2 Customer Service Bot Team: Router + Answerer + Escalator

• 6.3 Autonomous Business Assistant: Planner + Researcher + Executor Agents

7. Evaluation and Optimization

• 7.1 Measuring Team Performance and Agent Synergy

• 7.2 Debugging Inter-Agent Communication

• 7.3 Load Balancing and Redundancy in Multi-Agent Systems

8. Safety, Ethics, and Governance

• 8.1 Coordination Failures and Containment Strategies

• 8.2 Guardrails in Distributed Autonomy

• 8.3 Trust and Transparency in Multi-Agent Decision-Making

Multi-agent systems represent the next evolution in agentic AI—where intelligent agents not only act autonomously but also collaborate intelligently. This course equips you to design, build, and manage collaborative AI agents that can solve complex, distributed problems through coordination and communication. As the need for AI teams rises, your skills in multi-agent system design will be increasingly essential across industries.