Designing Multi-Agent Systems: Collaboration and Workflows

Overview

Google, IBM & Meta Certificates — All 10,000+ Courses at 40% Off

One annual plan covers every course and certificate on Coursera. 40% off for a limited time.

"Master the design and orchestration of collaborative AI systems in this hands-on course on multi-agent workflows using CrewAI, Agno, Mem0, AutoGen, and LangGraph. You’ll learn how to move beyond single-agent prompting to build teams of coordinated AI agents that plan, execute, and review complex tasks together. Module 1 introduces the foundations of multi-agent coordination, role hierarchies (Planner, Executor, Reviewer), and the CrewAI framework for agent orchestration. Module 2 guides you through designing role-based workflows, implementing a Researcher–Writer–Editor content team, and analyzing coordination efficiency using CrewAI logs and metrics. Module 3 focuses on shared and private memory models using Mem0, covering context hand-off, synchronization, and memory performance tuning for multi-agent pipelines. Module 4 explores advanced orchestration with Agno, a real-world Customer Support automation case study, and comparative benchmarking of CrewAI, AutoGen, and LangGraph. By the end of this course, you will: - Build and orchestrate multi-agent workflows using CrewAI and Agno - Integrate shared memory with Mem0 for context-aware collaboration - Design role-based pipelines simulating human-style teamwork - Compare leading frameworks to choose the right stack for production"

Syllabus

Foundations of Multi-Agent Collaboration

Introduces the transition from single-agent to collaborative multi-agent systems, emphasizing teamwork dynamics, communication strategies, and distributed reasoning, culminating in an introduction to CrewAI.

Designing Role-Based Multi-Agent Workflows

Learners design and simulate a functional, role-based workflow demonstrating structured collaboration between multiple agents using CrewAI's orchestration tools.

Shared Memory and Context Coordination

Explores shared memory integration in multi-agent systems, focusing on context continuity, communication efficiency, and memory optimization strategies using Mem0.

Orchestrating and Evaluating Multi-Agent Systems

Learners orchestrate multi-agent collaboration using Agno, simulate a real-world customer support workflow, and conduct comparative evaluations of leading multi-agent frameworks.