Designing Multi-Agent Systems: Collaboration and Workflows
"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"
Watch on Coursera ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
More on: Multi-Agent Systems
View skill →Related AI Lessons
⚡
⚡
⚡
⚡
Actually, vibe coding didn't kill testing — agentic engineering did
Dev.to · Muggle AI
Gemini 3.1 Flash Lite vs DeepSeek V4 Flash: Budget API Showdown for High-Volume Agent Loops (2026)
Dev.to AI
WebMCP Reality Check: Where the Spec Actually Stands
Dev.to AI
The 2026 Enterprise AI Mandate: From Generative Potential to Agentic Execution
Dev.to AI
🎓
Tutor Explanation
DeepCamp AI