Edureka

Developing MCP-Powered Agentic AI Systems

Edureka

Developing MCP-Powered Agentic AI Systems

Edureka

Instructor: Edureka

Included with Coursera Plus

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.

Details to know

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Recently updated!

February 2026

Assessments

14 assignments¹

AI Graded see disclaimer
Taught in English

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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

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

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

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

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

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Instructor

Edureka
Edureka
138 Courses 134,837 learners

Offered by

Edureka

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