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Il y a 4 modules dans ce cours
This program introduces you to CrewAI Tools, MCP, and Agentic RAG, designed for developers and AI practitioners looking to build intelligent, production-ready multi-agent systems. You’ll begin by exploring how agents use tools to interact with external systems, including CrewAI’s built-in tools and custom tool development for real-world workflows.
Next, you’ll dive into memory and knowledge systems, learning how agents store, retrieve, and prioritize information across interactions. You’ll explore Agentic RAG to build knowledge-driven agents that retrieve relevant data and generate accurate, context-aware responses. Through hands-on demonstrations, you will design systems that combine memory and retrieval to improve reliability and reduce hallucinations.
As you progress, you’ll focus on extending agents using the Model Context Protocol (MCP). You’ll learn how agents discover and interact with tools dynamically through MCP servers, enabling structured communication and scalable system design. You’ll also implement role-based access control, authentication, and secure workflows to ensure safe and controlled agent behavior in real-world environments.
By the end of the program, you will be able to:
- Identify how tools extend agent capabilities and enable structured workflows in CrewAI.
- Apply memory systems and Agentic RAG to build context-aware and knowledge-driven agents.
- Analyze how agents retrieve and use knowledge to improve accuracy and reduce hallucinations.
- Integrate MCP to enable dynamic tool discovery and structured agent communication.
- Design secure agent systems with role-based access control and authentication mechanisms.
- Develop scalable multi-agent workflows combining tools, memory, MCP, and retrieval.
This program is ideal for developers, AI engineers, and technical professionals interested in building advanced agent systems and intelligent automation workflows. Prior experience with Python programming and basic AI concepts will help maximize your learning experience.
Learners need a reliable internet connection, a modern web browser, and access to Python development tools. The course uses CrewAI and related AI technologies, which do not require specialized hardware. Basic familiarity with APIs and Python is recommended.
Join us and learn to build intelligent agents that can interact with tools, retain knowledge, operate securely, and power real-world AI systems at scale.
Learn how tools extend agent capabilities beyond text generation in CrewAI. Explore built-in tools for tasks such as web research, data extraction, and reporting, and understand how agents use them within structured workflows. Gain hands-on experience building custom tools and designing multi-step workflows using tool chaining and orchestration. Develop practical skills to create agents that interact with external systems and execute real-world tasks.
Inclus
14 vidéos5 lectures4 devoirs
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14 vidéos•Total 93 minutes
Specialization Introduction•6 minutes
Course Introduction•6 minutes
CrewAI Tool Ecosystem Overview•6 minutes
Demonstration: Building a Web Research Tool with SerperDevTool and ScrapeWebsiteTool•7 minutes
Demonstration: Assembling the Crew and Interpreting Results•6 minutes
Demonstration: Sales Reporting Workflow with CrewAI Data Tools•7 minutes
Demonstration: Running the CrewAI Sales Workflow and Analyzing the Results•6 minutes
Creating Custom Tools with @tool Decorator•6 minutes
Demonstration: Developing Job Market Analysis Tools for CrewAI Agents•7 minutes
Demonstration: Assembling the Job Market Intelligence Crew•7 minutes
Demonstration: Executing the Job Market Intelligence Workflow•7 minutes
API Integration Tools for Agents•6 minutes
Demonstration: Designing Chained Agent Workflows with Tool Hooks•7 minutes
Demonstration: Running the Chained News Workflow and Generating the Editorial Memo•7 minutes
5 lectures•Total 70 minutes
Course Syllabus•15 minutes
Complete Built-in Tool Reference Guide•15 minutes
Custom Tool Development Best Practices•15 minutes
Tool Design Patterns for Production•15 minutes
Module Summary: Agent Tooling and Integration with CrewAI•10 minutes
4 devoirs•Total 33 minutes
Knowledge Check: Agent Tooling and Integration with CrewAI•15 minutes
Practice Assignment: Exploring Built-in Tools in CrewAI•6 minutes
Practice Assignment: Building Custom Tools for CrewAI Agents•6 minutes
Practice Assignment: Designing Advanced Tool Workflows for Agents•6 minutes
Memory and Knowledge Systems for Intelligent Agents
Module 2•3 heures à terminer
Détails du module
Discover how intelligent agents store, retrieve, and use information through memory systems in CrewAI. Learn how to configure memory for persistence, relevance, and role-specific behavior, enabling agents to maintain context across interactions. Explore Agentic RAG and how agents use external knowledge sources to generate grounded and accurate responses. Develop skills to design context-aware and knowledge-driven agent systems.
Inclus
10 vidéos4 lectures4 devoirs
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10 vidéos•Total 61 minutes
CrewAI Memory Fundamentals and Usage•4 minutes
Demonstration: Getting Started with CrewAI Unified Memory in a Standalone Workflow•7 minutes
Demonstration: Recalling, Exploring, and Closing CrewAI Unified Memory•6 minutes
Advanced Memory Architecture in CrewAI•4 minutes
Demonstration: Configuring CrewAI Memory with Custom LLM and Embedder Settings•7 minutes
Demonstartion: Attaching Role-Specific Memory to Agents•7 minutes
Demonstration: Managing CrewAI Memory Storage, Scopes, and Persistence•6 minutes
Knowledge Sources and Agentic RAG•5 minutes
Demonstration: Building a Standalone RAG Agent with CrewAI Knowledge Sources•7 minutes
Demonstration: Orchestrating the RAG Workflow: Tasks, Shared Knowledge, and Final Output•7 minutes
4 lectures•Total 55 minutes
Memory Layer Characteristics and Use Cases•15 minutes
Tuning CrewAI Memory for Performance, Cost, and Accuracy•15 minutes
Designing Effective Knowledge Pipelines for CrewAI Agents•15 minutes
Module Summary: Memory and Knowledge Systems for Intelligent Agents•10 minutes
4 devoirs•Total 33 minutes
Knowledge Check: Memory and Knowledge Systems for Intelligent Agents•15 minutes
Practice Assignment: Understanding Memory Architecture in CrewAI•6 minutes
Practice Assignment: Configuring and Managing Memory for AI Agents•6 minutes
Practice Assignment: Building Knowledge-Driven Agents with RAG•6 minutes
Extending Agents with Model Context Protocol (MCP)
Module 3•3 heures à terminer
Détails du module
Learn how to extend agents using the Model Context Protocol (MCP) for structured and scalable communication. Explore how agents discover and interact with tools through MCP servers and integrate MCP into CrewAI workflows. Gain hands-on experience designing secure workflows with role-based access control and token validation. Develop the ability to build flexible and production-ready agent systems with controlled tool access.
Inclus
12 vidéos4 lectures4 devoirs
Afficher les informations sur le contenu du module
12 vidéos•Total 75 minutes
Introduction to Model Context Protocol (MCP)•5 minutes
Demonstration: Discovering and Using MCP Tools via Server Exploration•7 minutes
Demonstration: Designing an MCP Server to Expose Dynamic News Tools•6 minutes
Demonstration: MCP Tool Discovery and Agent-Driven Morning Briefing Output•5 minutes
Understanding the MCP Server–Client Model•6 minutes
Demonstration: Building a Research Workflow Using MCPs Field in CrewAI•7 minutes
Demonstration: Designing a Research MCP Server with Custom Tools in CrewAI•6 minutes
Demonstration: Running an MCP-Driven Research Pipeline in CrewAI•7 minutes
MCP Integration Approaches in CrewAI•6 minutes
Demonstration: Role-Based Access Control in MCP: Junior vs Senior Agent Behavior•7 minutes
Demonstration: Securing MCP Tools with Token Validation and Access Control•6 minutes
Demonstration: Analyzing Agent Behavior Under MCP Access Restrictions•6 minutes
4 lectures•Total 55 minutes
Understanding the Foundation of Interoperable Agent Systems•15 minutes
Optimizing Communication Between CrewAI Agents and MCP Servers•15 minutes
Designing Scalable and Secure Agent Systems with MCP in CrewAI•15 minutes
Module Summary: Extending Agents with Model Context Protocol (MCP)•10 minutes
4 devoirs•Total 33 minutes
Knowledge Check: Extending Agents with Model Context Protocol (MCP)•15 minutes
Practice Assignment: Introduction to the Model Context Protocol (MCP)•6 minutes
Practice Assignment: Integrating MCP with CrewAI Agents•6 minutes
Practice Assignment: Designing MCP-Powered Agent Workflows•6 minutes
Course Wrap-Up and Assessment
Module 4•2 heures à terminer
Détails du module
Consolidate your learning across tools, memory, MCP, and Agentic RAG. Apply your skills in a hands-on project by building a knowledge-driven agent system that integrates tool usage, memory, and retrieval. Complete a graded assessment to demonstrate your ability to design and implement scalable agent workflows. Reflect on your progress and prepare for more advanced multi-agent system design.
Inclus
1 vidéo1 lecture2 devoirs1 sujet de discussion
Afficher les informations sur le contenu du module
1 vidéo•Total 5 minutes
Course Summary•5 minutes
1 lecture•Total 30 minutes
Practice Project: Building an AI-Powered Enterprise Research and Intelligence System•30 minutes
2 devoirs•Total 60 minutes
End Course Knowledge Check: CrewAI Tools, MCP, and Agentic RAG•30 minutes
Designing a Secure Multi-Agent Research System with CrewAI, MCP, and Agentic RAG•30 minutes
1 sujet de discussion•Total 5 minutes
Describe Your Learning Journey•5 minutes
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Pour quelles raisons les étudiants sur Coursera nous choisissent-ils pour leur carrière ?
Felipe M.
Étudiant(e) depuis 2018
’Pouvoir suivre des cours à mon rythme à été une expérience extraordinaire. Je peux apprendre chaque fois que mon emploi du temps me le permet et en fonction de mon humeur.’
Jennifer J.
Étudiant(e) depuis 2020
’J'ai directement appliqué les concepts et les compétences que j'ai appris de mes cours à un nouveau projet passionnant au travail.’
Larry W.
Étudiant(e) depuis 2021
’Lorsque j'ai besoin de cours sur des sujets que mon université ne propose pas, Coursera est l'un des meilleurs endroits où se rendre.’
Chaitanya A.
’Apprendre, ce n'est pas seulement s'améliorer dans son travail : c'est bien plus que cela. Coursera me permet d'apprendre sans limites.’
This course is ideal for developers, AI engineers, and technical professionals who want to build intelligent multi-agent systems. It is also suitable for learners interested in automation, AI workflows, and real-world agent-based applications.
What will I learn in this course?
You will learn how to build intelligent agents using CrewAI, integrate tools, manage memory, apply Agentic RAG for knowledge retrieval, and design secure workflows using MCP for real-world AI systems.
What tools and technologies will be used in this course?
You will work with CrewAI, Python, built-in and custom tools, memory systems, retrieval-based architectures (RAG), and the Model Context Protocol (MCP) for structured communication.
Do I need prior experience with CrewAI, MCP, or RAG?
No prior experience with CrewAI, MCP, or RAG is required. The course introduces all concepts step by step, making it accessible while still covering advanced system design topics.
Will I get hands-on practice with agent workflows and tools?
Yes, the course includes demonstrations and practice assignments where you will build workflows, integrate tools, configure memory, and design agent systems in realistic scenarios.
How does this course cover memory and knowledge systems in AI agents?
You will learn how agents store, recall, and prioritize information using memory systems, and how Agentic RAG enables agents to retrieve and use external knowledge for accurate responses.
What is the Model Context Protocol (MCP), and why is it important?
MCP is a framework that allows agents to discover and interact with tools dynamically. It enables structured communication, secure access control, and scalable system design in multi-agent environments.
Will I learn how to build secure and scalable multi-agent systems?
Yes, the course covers role-based access control, authentication, and MCP-based workflows, helping you design secure, scalable, and production-ready agent systems.
How is this course different from other AI or agent development courses?
This course focuses on real-world system design using CrewAI, combining tools, memory, RAG, and MCP. It goes beyond prompting to teach how to build complete, production-ready agent systems.
Will I receive a certificate upon completion?
Yes, you will receive a certificate upon successfully completing the course and its assessments.
What career opportunities can this course lead to?
This course prepares you for roles such as AI Engineer, Agent Systems Developer, Automation Engineer, or roles focused on building intelligent AI applications.
Is this course suitable for someone with no prior experience in AI?
Yes, as long as you have basic Python knowledge, you can follow along. The course gradually introduces key AI concepts and builds up to advanced agent system design.
When will I have access to the lectures and assignments?
To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
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.
Is financial aid available?
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.