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⇱ Developing MCP-Powered Agentic AI Systems | Coursera


Developing MCP-Powered Agentic AI Systems

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Developing MCP-Powered Agentic AI Systems

Instructor: Edureka

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Gain insight into a topic and learn the fundamentals.
Advanced level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

Gain insight into a topic and learn the fundamentals.
Advanced level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Explain MCP architecture and communication patterns that enable reliable, interoperable agentic AI systems.

  • Implement MCP servers, tools, and URI-based resources to connect agents with structured real-world data.

  • Design intelligent agents that reason reflexively, plan multi-step tasks, and recover from failures.

  • Deploy and evaluate agent systems using APIs, observability, monitoring, and scalable deployment practices.

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Recently updated!

February 2026

Assessments

14 assignments¹

AI Graded see disclaimer
Taught in English

Build your subject-matter expertise

This course is part of the Agentic AI Engineering Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

This program introduces you to Developing MCP-Powered Agentic AI Systems, designed for developers and AI practitioners who want to build reliable, scalable, and production-ready agent systems using the Model Context Protocol (MCP).

You’ll begin by mastering the core architecture of MCP, learning how agents communicate with servers, discover tools, and access structured resources through standardized interfaces. You’ll build MCP servers, design namespaced tools, and expose real-world data through URI-based resources, establishing a strong foundation for interoperable agent systems. Next, you’ll dive into deep agent reasoning and resilience patterns. You’ll explore reflexive and self-improving agents, output-correction feedback loops, fallback strategies, and self-healing recovery mechanisms. Through hands-on demonstrations, you’ll design agents capable of multi-step planning, hierarchical reasoning, and reliable execution across complex workflows. As you progress, you’ll focus on deployment and observability. You’ll learn to expose agents as APIs, track execution visibility, evaluate agent quality, and monitor performance using modern observability tools. You’ll also deploy end-to-end agent applications, combining reasoning pipelines, monitoring, and user-facing interfaces into complete production systems. By the end of the program, you will be able to: - Explain MCP architecture and how it enables reliable, multi-agent communication. - Build MCP servers with structured tools and URI-based resource access. - Design agents that reason reflexively, recover from failures, and execute multi-step tasks. - Implement fallback logic, error recovery, and self-healing agent workflows. - Deploy production-grade agent APIs with execution visibility and observability. - Evaluate, monitor, and scale agent systems for real-world applications. This program is ideal for AI engineers, developers, and technical professionals who want to move beyond prompt-based systems and build robust agentic AI architectures. Prior experience with Python programming and basic AI concepts will help you get the most out of the course. Learners need a reliable internet connection, a modern web browser, and access to Python development tools. The course uses MCP-based agent tooling and modern AI frameworks, without requiring specialized hardware. Join this program to learn how to design, deploy, and operate intelligent, resilient, and production-ready agent systems powered by MCP.

Learn the foundational concepts of the Model Context Protocol (MCP) and how it enables reliable, scalable agentic AI systems. Explore MCP architecture, the server–client communication model, and how agents interact with tools and resources. Build hands-on experience creating MCP servers, designing namespaced tools and URI-based resources, and orchestrating workflows across single and multiple servers to support real-world agent applications.

What's included

13 videos5 readings4 assignments

13 videosTotal 82 minutes
  • Specialization Introduction6 minutes
  • Course Introduction4 minutes
  • Introduction to Model Context Protocol for Multi-Agent Systems5 minutes
  • Understanding the MCP Server–Client Model7 minutes
  • Demonstration: Building Your First MCP Server with stdio and HTTP Transports7 minutes
  • Demonstration: Connecting to MCP Servers and Calling Tools7 minutes
  • Demonstration: Orchestrating the Full MCP Workflow6 minutes
  • Namespaced Tools and Resource Schema Design6 minutes
  • Demonstration: Creating MCP Resources with URI-Based Access Patterns7 minutes
  • Demonstration: Modeling File Systems as Read-Only MCP Resources6 minutes
  • Multi-Server Routing and Discovery Mechanisms8 minutes
  • Demonstration: Consuming MCP Resources: From Direct Access to AI-Powered Answers7 minutes
  • Demonstration: Turning MCP Resources into Smart Answers5 minutes
5 readingsTotal 75 minutes
  • Course Syllabus15 minutes
  • Why MCP is the Foundation for Reliable Agentic AI Systems15 minutes
  • How MCP Connects AI Systems to the Real World15 minutes
  • Multi-Server Discovery and Smart Routing in MCP Systems15 minutes
  • Module Summary: MCP Core Architecture and Server Development15 minutes
4 assignmentsTotal 33 minutes
  • Knowledge Check: MCP Core Architecture and Server Development15 minutes
  • Practice Assignment: MCP Architecture and Communication Model6 minutes
  • Practice Assignment: MCP Tool and Resource Design with URI Access6 minutes
  • Practice Assignment: Multi-Server MCP Discovery and Smart Resource Routing6 minutes

Discover how to design intelligent agents that can evaluate their own outputs, recover from failures, and reason across complex tasks. Learn reflexive agent patterns, output-correction feedback loops, retry strategies, and fallback logic. Build multi-step planning and execution workflows that enable agents to adapt, self-heal, and maintain reliability in dynamic and error-prone environments.

What's included

13 videos4 readings4 assignments

13 videosTotal 74 minutes
  • Actor–Critic Pattern for Agent Improvement7 minutes
  • Demonstration: Building an Output-Correction Feedback Loop - I7 minutes
  • Demonstration: Building an Output-Correction Feedback Loop - II6 minutes
  • Retry Strategies, Backoff Techniques, and Failure Handling6 minutes
  • Demonstration: Implementing Self-Healing Recovery with Fallback Chains - I7 minutes
  • Demonstration: Implementing Self-Healing Recovery with Fallback Chains - II6 minutes
  • Demonstration: Designing a Resilient Decision-Making Agent - I6 minutes
  • Demonstration: Designing a Resilient Decision-Making Agent - II6 minutes
  • Hierarchical Planning and Multi-Level Reasoning5 minutes
  • Demonstration: Planner → Executor → Validator Workflow - I5 minutes
  • Demonstration: Planner → Executor → Validator Workflow - II3 minutes
  • Demonstration: Building a Multi-Stage Knowledge Pipeline and Multi-Hop System Reasoning - I7 minutes
  • Demonstration: Building a Multi-Stage Knowledge Pipeline and Multi-Hop System Reasoning - II4 minutes
4 readingsTotal 60 minutes
  • How Agents Learn from Their Own Outputs15 minutes
  • Error Recovery Best Practices15 minutes
  • Multi-Step Task Planning and Execution15 minutes
  • Module Summary: Deep Agents, Reflexive Reasoning, and Error Recovery15 minutes
4 assignmentsTotal 33 minutes
  • Knowledge Check: Deep Agents, Reflexive Reasoning, and Error Recovery15 minutes
  • Practice Assignment: Reflexive and Self-Improving Agent Models6 minutes
  • Practice Assignment: Self-Healing Agents and Fallback Logic6 minutes
  • Practice Assignment: Multi-Step Task Planning and Execution6 minutes

Learn how to deploy agent systems as production-ready services with visibility, observability, and scalability. Explore API design using LangServe, execution tracing, and workflow monitoring with LangSmith. Gain hands-on experience evaluating agent quality, containerizing and scaling systems, and delivering an end-to-end production application with observability and performance analysis.

What's included

14 videos4 readings4 assignments

14 videosTotal 72 minutes
  • Getting Started with LangServe4 minutes
  • Designing Versioned Agent Endpoints4 minutes
  • Overview of Langfuse and Its Capabilities5 minutes
  • AI Observability with LangSmith5 minutes
  • Managing Workflows with LangSmith5 minutes
  • Setting Up LangSmith5 minutes
  • Evaluation Dataset Design and Regression Testing4 minutes
  • Containerizing and Scaling Agents4 minutes
  • Demonstration: Project Architecture and System Overview5 minutes
  • Demonstration: Environment Setup and Data Ingestion Pipeline7 minutes
  • Demonstration: Production-grade Resume Analysis Workflow6 minutes
  • Demonstration: End-to-End Application Orchestration with Streamlit6 minutes
  • Demonstration: Streamlit Interface for AI Resume Screening6 minutes
  • Demonstration: LangSmith Observability and Performance Analysis5 minutes
4 readingsTotal 60 minutes
  • Designing Production-Ready Agent APIs with Execution Visibility15 minutes
  • Containerizing Production Agent Systems with Docker15 minutes
  • Bridging Model Pipelines, Observability, and Deployment15 minutes
  • Module Summary: Deploying, Observing, and Scaling Production Agent Systems15 minutes
4 assignmentsTotal 33 minutes
  • Knowledge Check: Deploying, Observing, and Scaling Production Agent Systems15 minutes
  • Practice Assignment: Production Agent APIs and Execution Visibility6 minutes
  • Practice Assignement: Observability, Evaluation, and Quality Monitoring with LangSmith6 minutes
  • Practice Assignement: End-to-End Production Application Deployment6 minutes

Consolidate your learning across MCP architecture, deep agent reasoning, and production deployment. Validate your understanding through a comprehensive graded assessment that tests your ability to design, reason about, and operate production-grade agentic AI systems.

What's included

1 video1 reading2 assignments1 discussion prompt

1 videoTotal 3 minutes
  • Course Summary3 minutes
1 readingTotal 30 minutes
  • Practice Project: Designing an MCP-Powered Intelligent Incident Response Agent30 minutes
2 assignmentsTotal 60 minutes
  • End Course Knowledge Check: Developing MCP-Powered Agentic AI Systems30 minutes
  • Building an MCP-Powered Agent System for Enterprise Document Intelligence30 minutes
1 discussion promptTotal 5 minutes
  • Describe Your Learning Journey5 minutes

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Instructor

Edureka
203 Courses185,285 learners

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Frequently asked questions

This course is designed for developers, AI engineers, and technical professionals interested in building agentic AI systems using the Model Context Protocol (MCP). Learners with basic Python knowledge and familiarity with AI concepts will benefit most.

In this course, you will learn how to design, build, and deploy MCP-powered agent systems. You’ll explore MCP architecture, server and tool design, deep agent reasoning, error recovery, observability, and end-to-end agent deployment.

The course uses MCP-based agent tooling, Python, API-based agent frameworks, observability tools, and deployment technologies. These tools help you build, monitor, and scale reliable agent systems.

No prior experience with MCP is required. A basic understanding of Python and general AI concepts is recommended to follow the hands-on demonstrations effectively.

Yes. The course includes hands-on demonstrations, guided coding sessions, and practice assignments where you build MCP servers, design agents, and deploy complete agent workflows.

The course is structured to be completed in approximately 4 weeks, with a recommended study pace of 3–4 hours per week. You can learn at your own pace and revisit content as needed.

Yes. After completing all modules, practice assignments, and assessments, you will receive a Certificate of Completion to showcase your skills in MCP-powered agentic AI systems.

Unlike general AI courses, this program focuses on MCP as a foundation for reliable agent systems. It emphasizes agent communication, tool interoperability, deep reasoning, error recovery, and observability through hands-on, real-world examples.

This course prepares you for roles such as AI Developer, Agent Systems Engineer, Automation Engineer, and Intelligent Systems Architect, where building and operating agentic AI systems is required.

This course introduces core agentic AI concepts from the ground up, but basic Python knowledge is recommended. Learners new to AI will gain a structured understanding of agent systems and MCP fundamentals.

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.

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.

Financial aid available,

¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.