Designing Multi-Agent Systems: Collaboration and Workflows
Build coordinated teams of AI agents: give each a specialized role, share memory across them with Mem0, orchestrate them with CrewAI and Agno, and evaluate the frameworks that run them.
Designing Multi-Agent Systems: Collaboration and Workflows
By Board Infinity · Offered on Coursera
- 26 video lessons · 15 assignments
- CrewAI, Agno and Mem0 in one course
- Shareable certificate on completion
- Flexible schedule - learn at your own pace
Multi-agent orchestration
CrewAI and Agno run the team
Role-based collaboration
Planner, Executor and Reviewer
Shared memory with Mem0
Context that carries between agents
Framework evaluation
CrewAI, AutoGen and LangGraph compared
From one agent to a team that plans, executes and reviews.
A hands-on course in designing and orchestrating collaborative AI systems with CrewAI, Agno, Mem0, AutoGen and LangGraph.
Designing Multi-Agent Systems: Collaboration and Workflows moves you beyond single-agent prompting to teams of coordinated AI agents - distributed reasoning, coordination models and role hierarchies, starting with CrewAI.
You design role-based workflows with task allocation, agent-to-agent communication and feedback loops, build a Researcher–Writer–Editor content team, then add shared and private memory with Mem0 for context hand-off and synchronization.
The last module orchestrates agents with Agno, works through a customer support automation case study, and benchmarks CrewAI, AutoGen and LangGraph.
The learning journey
From a single prompt to an evaluated agent team.
- Single agentWhere you start
- Agent rolesPlanner, Executor, Reviewer
- CrewAI workflowMessaging, task allocation
- Content teamResearcher, Writer, Editor
- Shared memoryMem0, context hand-off
- Agno + supportCustomer support case
- BenchmarkCrewAI, AutoGen, LangGraph
For builders ready to move past a single agent.
Listed on Coursera at Intermediate level, with recommended experience. It is Course 2 of 3, so it follows on naturally from single-agent architecture.
Building agent systems
Developers who want coordinated AI agent systems rather than one agent doing everything.
Exploring orchestration
Engineers exploring multi-agent orchestration with CrewAI and Agno.
Building role-based workflows
Python developers putting Planner, Executor and Reviewer roles into working pipelines.
Moving beyond one agent
Practitioners after shared memory and context continuity, coming from single-agent architecture.
Three capabilities, and one team you build.
The three outcomes Coursera lists for this course, plus the applied workflow described in Module 2.
Orchestrate multi-agent workflows with CrewAI and Agno
Design and orchestrate coordinated multi-agent workflows using CrewAI and Agno frameworks
Integrate shared memory with Mem0
Integrate Mem0 shared memory for context continuity across collaborating agents
Build Planner, Executor and Reviewer pipelines
Build role-based pipelines (Planner, Executor, Reviewer) for real-world use cases
A Researcher–Writer–Editor content team
Described in the course as a Module 2 workflow: three agents that research, write and edit together. Not one of Coursera's listed outcomes.
Inside a role-based pipeline
Who does what, and how work moves between them.
- 01
Plan
Breaks the goal into tasks and allocates them.
- 02
Execute
Carries out the assigned work.
- 03
Review
Checks the output and sends feedback.
- 04
Remember
Context carries across hand-offs with Mem0.
Module 4 then compares CrewAI, AutoGen and LangGraph so you can choose the right stack.
Build agent teams with someone in your corner.
A dedicated coach alongside the course - someone to talk through the hard parts, plan your roadmap and look at your practice work with you.
Book a demo session- A coach matched to your goal in 24 hours
- A written roadmap you keep
- Weekly sessions for as long as you want them
Separate from the Coursera course and its enrollment · 60-minute demo session · no obligation to continue
Get unstuck faster
Talk through the concept that is not clicking with someone who has worked with it.
Your work reviewed
A second pair of eyes on your practice - not just whether it works, but why.
A structured roadmap
A written plan that fits around the course, so every session has a purpose.
Aim it at a role
Which direction fits you, and what to learn next.
Four modules, from one agent to an evaluated team.
Two weeks at 10 hours a week, at your own pace. Open a module to see its description, what it covers, and every lesson exactly as it appears on Coursera.
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.
- Track industry trends in distributed intelligence and the move from solo agents to teams
- Weigh the advantages and challenges of distributed intelligence
- Compare coordination models and the Planner, Executor and Reviewer roles
- Model role-based dependencies and balance independence with oversight
Videos
- Industry Trends in Distributed Intelligence6 min
- Transition from Solo Agents to Teams4 min
- Advantages and Challenges of Distributed Intelligence5 min
- Coordination Models Overview5 min
- Agent Roles: Planner, Executor, Reviewer3 min
- Modeling Role-Based Dependencies6 min
- Balancing Independence and Oversight6 min
Readings
- Collaborative AI Agents in the Enterprise30 min
- Distributed AI Coordination Principles30 min
- Team Dynamics in Agentic Systems30 min
Assignments
- From Single to Multi-Agent Systems30 min
- Multi-Agent Roles and Responsibilities30 min
- Foundations of Multi-Agent Collaboration60 min
Learners design and simulate a functional, role-based workflow demonstrating structured collaboration between multiple agents using CrewAI's orchestration tools.
- Set up your first CrewAI workflow and demonstrate agent-to-agent messaging
- Design multi-agent role structures and map workflow dependencies
- Apply task allocation strategies and implement the Researcher-Writer-Editor chain
- Manage inter-agent feedback loops, then run and debug CrewAI pipelines
Videos
- Overview of CrewAI Framework3 min
- Setting Up Your First CrewAI Workflow4 min
- Agent-to-Agent Messaging Demonstration3 min
- Designing Multi-Agent Role Structures3 min
- Workflow Dependency Mapping3 min
- Task Allocation Strategies3 min
- Implementing the Researcher-Writer-Editor Chain3 min
- Managing Inter-Agent Feedback Loops5 min
- Running and Debugging CrewAI Pipelines6 min
Readings
- CrewAI Developer Setup Guide30 min
- Workflow Orchestration in CrewAI30 min
- Analyzing Team Performance30 min
Assignments
- Introduction to CrewAI30 min
- Planning Collaborative Workflows30 min
- Building the Content Team30 min
- Designing Role-Based Multi-Agent Workflows60 min
Explores shared memory integration in multi-agent systems, focusing on context continuity, communication efficiency, and memory optimization strategies using Mem0.
- Monitor agent logs and metrics to find bottlenecks and redundancies
- Understand how memory influences collaboration
- Apply collaborative context retention techniques
- Work with shared and private memory, and integrate Mem0 into CrewAI pipelines
Videos
- Monitoring Agent Logs and Metrics6 min
- Identifying Bottlenecks and Redundancies3 min
- How Memory Influences Collaboration6 min
- Collaborative Context Retention Techniques5 min
- Implementing the Researcher–Writer–Editor Chain4 min
Readings
- Understanding Shared and Private Memory30 min
- Integrating Mem0 into CrewAI Pipelines30 min
- Debugging and Optimizing Memory Coordination10 min
Assignments
- Analyzing Team Performance30 min
- Understanding Shared and Private Memory30 min
- Implementing Shared Memory with Mem030 min
- Shared Memory and Context Coordination60 min
Learners orchestrate multi-agent collaboration using Agno, simulate a real-world customer support workflow, and conduct comparative evaluations of leading multi-agent frameworks.
- Get started with the Agno runtime and FastAPI setup for agentic workflows
- Design a multi-agent support pipeline with real-time communication
- Apply persistent memory patterns for agent deployments
- Benchmark performance and compare multi-agent frameworks
Videos
- Introduction to Agno Runtime6 min
- FastAPI Setup for Agentic Workflows3 min
- Designing a Multi-Agent Support Pipeline3 min
- Implementing Real-Time Communication6 min
- Persistent Memory Patterns for Agent Deployments4 min
Readings
- Custom Orchestration with Agno10 min
- Performance Benchmarking30 min
- Framework Benchmarking and Reflection10 min
Assignments
- Debugging and Optimizing Memory Coordination30 min
- Custom Orchestration with Agno30 min
- Case Study - Customer Support Workflow30 min
- Orchestrating and Evaluating Multi-Agent Systems60 min
Module times are as shown on the course page (4, 5, 4 and 4 hours). The specialization page separately lists this course as Course 2 · 16 hours.
The frameworks you use, and the skills you leave with.
Coursera lists six tools for this course. Four of them are shown below; the full list and the wider stack follow.
CrewAI
Role-based orchestration, from your first workflow to debugging pipelines.
LangGraph
One of the frameworks benchmarked in Module 4.
Python Programming
The language the agent workflows are built in.
AI Orchestration
Coordinating agents, tasks and hand-offs - also listed: Agentic Workflows, AI Workflows.
LangGraphAgentic WorkflowsCrewAIPython ProgrammingAI WorkflowsAI Orchestration
AgnoMem0AutoGenFastAPIShared and private memoryPlanner / Executor / Reviewer rolesResearcher–Writer–Editor pipelineAgent logs and metrics
Named in the course description and lesson titles. These are not on Coursera's official tools list.
The five skills Coursera lists for this course, grouped by the kind of work they describe.
Agent systems
Designing agents that work as a team.
- Agentic systems
- Generative AI Agents
Memory & context
Keeping agents on the same page.
- Memory Management
- Context Management
LLM applications
Putting it into a working application.
- LLM Application
A credential for your multi-agent work.
Coursera lists a shareable certificate for this course, which you can add to your LinkedIn profile.
Not a degree, university credit or regulated professional qualification.
Shareable certificate
Earned through Coursera when you complete the course.
Add it to LinkedIn
Add the credential to your LinkedIn profile.
Add it to your resume or CV
Coursera also mentions resumes and CVs.
Part of a specialization
Course 2 of 3 in Building and Deploying AI Agents with LLMs and LangChain.
Where coordinated agents are put to work.
Potential directions for these skills - not guaranteed job outcomes. No salaries, placements or employer partnerships are implied.
Multi-agent systems development
Designing teams of agents with clear roles and dependencies.
AI workflow engineering
Task allocation, dependency mapping and feedback loops.
Agent orchestration
Running agent teams with CrewAI and Agno.
LLM application development
Context and memory management that keeps applications coherent.
Systems integration
FastAPI setup, real-time communication and persistent memory patterns.
AI automation design
Automations such as the customer support workflow case study.
This is the collaboration stage, not the whole journey.
This course focuses on coordinated agents. Course 1 covers individual-agent architecture and memory; Course 3 extends into LLM integration and production API deployment. No single course covers every framework or every production requirement.
Single agent, agent team, production.
Course 2 of 3 in Building and Deploying AI Agents with LLMs and LangChain: from single-agent architecture to multi-agent collaboration to production deployment.
AI Agent Architecture: Reasoning, Memory, and LangGraph
Modular agent architecture, structured validation and persistent memory.
View course Course 2 · 16 hours · You are hereDesigning Multi-Agent Systems: Collaboration and Workflows
Coordinated agent teams, shared memory and framework evaluation.
View on Coursera Course 3 · 16 hoursDeploying AI Agents: LLMs, LangGraph, and Production APIs
LLM integration, FastAPI serving, persistent state and production evaluation.
View courseApplied projects in this course
According to the specialization page, in the multi-agent course learners:
- Build a "Content Team" in CrewAI with Researcher, Writer and Editor roles
- Implement shared memory hand-offs
- Design a Customer Support Workflow
3-course series · Intermediate · 12 weeks at 4 hours a week · view the specialization on Coursera.
Know exactly what you're signing up for.
Common questions
Course questions are answered by Board Infinity from the course description. Access and enrollment answers are Coursera's own.
It focuses on designing and orchestrating collaborative AI systems using CrewAI, Agno and Mem0. You move from single-agent prompting to teams of agents that plan, execute and review tasks together, covering role-based collaboration, shared memory, and a comparison of CrewAI, AutoGen and LangGraph.
Coursera lists LangGraph, Agentic Workflows, CrewAI, Python Programming, AI Workflows and AI Orchestration as the tools you'll learn. The course description also covers Agno and Mem0, and compares CrewAI, AutoGen and LangGraph.
A single-agent workflow uses one agent to handle a task. A multi-agent system coordinates several specialized agents, such as a Planner, Executor and Reviewer, that divide the work, exchange information and review each other's output.
To access course materials, assignments, and earn a Certificate, you'll need to purchase the Certificate experience when you enroll in a course. Eligible learners may also have the option to start with a Free Trial. Some courses may also offer a Full Course, No Certificate option. This lets you access course materials, submit required assessments, and receive a final grade, but you won't be able to earn or purchase a Certificate.
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can't afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you'll find a link to apply on the description page.
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