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In diesem Kurs gibt es 4 Module
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.
Das ist alles enthalten
14 Videos5 Lektüren4 Aufgaben
Infos zu Modulinhalt anzeigen
14 Videos•Insgesamt 93 Minuten
Specialization Introduction•6 Minuten
Course Introduction•6 Minuten
CrewAI Tool Ecosystem Overview•6 Minuten
Demonstration: Building a Web Research Tool with SerperDevTool and ScrapeWebsiteTool•7 Minuten
Demonstration: Assembling the Crew and Interpreting Results•6 Minuten
Demonstration: Sales Reporting Workflow with CrewAI Data Tools•7 Minuten
Demonstration: Running the CrewAI Sales Workflow and Analyzing the Results•6 Minuten
Creating Custom Tools with @tool Decorator•6 Minuten
Demonstration: Developing Job Market Analysis Tools for CrewAI Agents•7 Minuten
Demonstration: Assembling the Job Market Intelligence Crew•7 Minuten
Demonstration: Executing the Job Market Intelligence Workflow•7 Minuten
API Integration Tools for Agents•6 Minuten
Demonstration: Designing Chained Agent Workflows with Tool Hooks•7 Minuten
Demonstration: Running the Chained News Workflow and Generating the Editorial Memo•7 Minuten
5 Lektüren•Insgesamt 70 Minuten
Course Syllabus•15 Minuten
Complete Built-in Tool Reference Guide•15 Minuten
Custom Tool Development Best Practices•15 Minuten
Tool Design Patterns for Production•15 Minuten
Module Summary: Agent Tooling and Integration with CrewAI•10 Minuten
4 Aufgaben•Insgesamt 33 Minuten
Practice Assignment: Exploring Built-in Tools in CrewAI•6 Minuten
Practice Assignment: Building Custom Tools for CrewAI Agents•6 Minuten
Practice Assignment: Designing Advanced Tool Workflows for Agents•6 Minuten
Knowledge Check: Agent Tooling and Integration with CrewAI•15 Minuten
Memory and Knowledge Systems for Intelligent Agents
Modul 2•3 Stunden abzuschließen
Moduldetails
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.
Das ist alles enthalten
10 Videos4 Lektüren4 Aufgaben
Infos zu Modulinhalt anzeigen
10 Videos•Insgesamt 61 Minuten
CrewAI Memory Fundamentals and Usage•4 Minuten
Demonstration: Getting Started with CrewAI Unified Memory in a Standalone Workflow•7 Minuten
Demonstration: Recalling, Exploring, and Closing CrewAI Unified Memory•6 Minuten
Advanced Memory Architecture in CrewAI•4 Minuten
Demonstration: Configuring CrewAI Memory with Custom LLM and Embedder Settings•7 Minuten
Demonstartion: Attaching Role-Specific Memory to Agents•7 Minuten
Demonstration: Managing CrewAI Memory Storage, Scopes, and Persistence•6 Minuten
Knowledge Sources and Agentic RAG•5 Minuten
Demonstration: Building a Standalone RAG Agent with CrewAI Knowledge Sources•7 Minuten
Demonstration: Orchestrating the RAG Workflow: Tasks, Shared Knowledge, and Final Output•7 Minuten
4 Lektüren•Insgesamt 55 Minuten
Memory Layer Characteristics and Use Cases•15 Minuten
Tuning CrewAI Memory for Performance, Cost, and Accuracy•15 Minuten
Designing Effective Knowledge Pipelines for CrewAI Agents•15 Minuten
Module Summary: Memory and Knowledge Systems for Intelligent Agents•10 Minuten
4 Aufgaben•Insgesamt 33 Minuten
Practice Assignment: Understanding Memory Architecture in CrewAI•6 Minuten
Practice Assignment: Configuring and Managing Memory for AI Agents•6 Minuten
Practice Assignment: Building Knowledge-Driven Agents with RAG•6 Minuten
Knowledge Check: Memory and Knowledge Systems for Intelligent Agents•15 Minuten
Extending Agents with Model Context Protocol (MCP)
Modul 3•3 Stunden abzuschließen
Moduldetails
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.
Das ist alles enthalten
12 Videos4 Lektüren4 Aufgaben
Infos zu Modulinhalt anzeigen
12 Videos•Insgesamt 75 Minuten
Introduction to Model Context Protocol (MCP)•5 Minuten
Demonstration: Discovering and Using MCP Tools via Server Exploration•7 Minuten
Demonstration: Designing an MCP Server to Expose Dynamic News Tools•6 Minuten
Demonstration: MCP Tool Discovery and Agent-Driven Morning Briefing Output•5 Minuten
Understanding the MCP Server–Client Model•6 Minuten
Demonstration: Building a Research Workflow Using MCPs Field in CrewAI•7 Minuten
Demonstration: Designing a Research MCP Server with Custom Tools in CrewAI•6 Minuten
Demonstration: Running an MCP-Driven Research Pipeline in CrewAI•7 Minuten
MCP Integration Approaches in CrewAI•6 Minuten
Demonstration: Role-Based Access Control in MCP: Junior vs Senior Agent Behavior•7 Minuten
Demonstration: Securing MCP Tools with Token Validation and Access Control•6 Minuten
Demonstration: Analyzing Agent Behavior Under MCP Access Restrictions•6 Minuten
4 Lektüren•Insgesamt 55 Minuten
Understanding the Foundation of Interoperable Agent Systems•15 Minuten
Optimizing Communication Between CrewAI Agents and MCP Servers•15 Minuten
Designing Scalable and Secure Agent Systems with MCP in CrewAI•15 Minuten
Module Summary: Extending Agents with Model Context Protocol (MCP)•10 Minuten
4 Aufgaben•Insgesamt 33 Minuten
Practice Assignment: Introduction to the Model Context Protocol (MCP)•6 Minuten
Practice Assignment: Integrating MCP with CrewAI Agents•6 Minuten
Practice Assignment: Designing MCP-Powered Agent Workflows•6 Minuten
Knowledge Check: Extending Agents with Model Context Protocol (MCP)•15 Minuten
Course Wrap-Up and Assessment
Modul 4•2 Stunden abzuschließen
Moduldetails
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.
Das ist alles enthalten
1 Video1 Lektüre2 Aufgaben1 Diskussionsthema
Infos zu Modulinhalt anzeigen
1 Video•Insgesamt 5 Minuten
Course Summary•5 Minuten
1 Lektüre•Insgesamt 30 Minuten
Practice Project: Building an AI-Powered Enterprise Research and Intelligence System•30 Minuten
2 Aufgaben•Insgesamt 60 Minuten
End Course Knowledge Check: CrewAI Tools, MCP, and Agentic RAG•30 Minuten
Designing a Secure Multi-Agent Research System with CrewAI, MCP, and Agentic RAG•30 Minuten
1 Diskussionsthema•Insgesamt 5 Minuten
Describe Your Learning Journey•5 Minuten
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themselves with industry-relevant skills in today’s cutting edge technologies.
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.
Finanzielle Unterstützung verfügbar, weitere Informationen
¹ Einige Aufgaben in diesem Kurs werden mit AI bewertet. Für diese Aufgaben werden Ihre Daten in Übereinstimmung mit Datenschutzhinweis von Courseraverwendet.