Developing MCP-Powered Agentic AI Systems
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Developing MCP-Powered Agentic AI Systems
This course is part of Agentic AI Engineering Specialization
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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.
Skills you'll gain
Details to know
February 2026
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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 videos•Total 82 minutes
- Specialization Introduction•6 minutes
- Course Introduction•4 minutes
- Introduction to Model Context Protocol for Multi-Agent Systems•5 minutes
- Understanding the MCP Server–Client Model•7 minutes
- Demonstration: Building Your First MCP Server with stdio and HTTP Transports•7 minutes
- Demonstration: Connecting to MCP Servers and Calling Tools•7 minutes
- Demonstration: Orchestrating the Full MCP Workflow•6 minutes
- Namespaced Tools and Resource Schema Design•6 minutes
- Demonstration: Creating MCP Resources with URI-Based Access Patterns•7 minutes
- Demonstration: Modeling File Systems as Read-Only MCP Resources•6 minutes
- Multi-Server Routing and Discovery Mechanisms•8 minutes
- Demonstration: Consuming MCP Resources: From Direct Access to AI-Powered Answers•7 minutes
- Demonstration: Turning MCP Resources into Smart Answers•5 minutes
5 readings•Total 75 minutes
- Course Syllabus•15 minutes
- Why MCP is the Foundation for Reliable Agentic AI Systems•15 minutes
- How MCP Connects AI Systems to the Real World•15 minutes
- Multi-Server Discovery and Smart Routing in MCP Systems•15 minutes
- Module Summary: MCP Core Architecture and Server Development•15 minutes
4 assignments•Total 33 minutes
- Knowledge Check: MCP Core Architecture and Server Development•15 minutes
- Practice Assignment: MCP Architecture and Communication Model•6 minutes
- Practice Assignment: MCP Tool and Resource Design with URI Access•6 minutes
- Practice Assignment: Multi-Server MCP Discovery and Smart Resource Routing•6 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 videos•Total 74 minutes
- Actor–Critic Pattern for Agent Improvement•7 minutes
- Demonstration: Building an Output-Correction Feedback Loop - I•7 minutes
- Demonstration: Building an Output-Correction Feedback Loop - II•6 minutes
- Retry Strategies, Backoff Techniques, and Failure Handling•6 minutes
- Demonstration: Implementing Self-Healing Recovery with Fallback Chains - I•7 minutes
- Demonstration: Implementing Self-Healing Recovery with Fallback Chains - II•6 minutes
- Demonstration: Designing a Resilient Decision-Making Agent - I•6 minutes
- Demonstration: Designing a Resilient Decision-Making Agent - II•6 minutes
- Hierarchical Planning and Multi-Level Reasoning•5 minutes
- Demonstration: Planner → Executor → Validator Workflow - I•5 minutes
- Demonstration: Planner → Executor → Validator Workflow - II•3 minutes
- Demonstration: Building a Multi-Stage Knowledge Pipeline and Multi-Hop System Reasoning - I•7 minutes
- Demonstration: Building a Multi-Stage Knowledge Pipeline and Multi-Hop System Reasoning - II•4 minutes
4 readings•Total 60 minutes
- How Agents Learn from Their Own Outputs•15 minutes
- Error Recovery Best Practices•15 minutes
- Multi-Step Task Planning and Execution•15 minutes
- Module Summary: Deep Agents, Reflexive Reasoning, and Error Recovery•15 minutes
4 assignments•Total 33 minutes
- Knowledge Check: Deep Agents, Reflexive Reasoning, and Error Recovery•15 minutes
- Practice Assignment: Reflexive and Self-Improving Agent Models•6 minutes
- Practice Assignment: Self-Healing Agents and Fallback Logic•6 minutes
- Practice Assignment: Multi-Step Task Planning and Execution•6 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 videos•Total 72 minutes
- Getting Started with LangServe•4 minutes
- Designing Versioned Agent Endpoints•4 minutes
- Overview of Langfuse and Its Capabilities•5 minutes
- AI Observability with LangSmith•5 minutes
- Managing Workflows with LangSmith•5 minutes
- Setting Up LangSmith•5 minutes
- Evaluation Dataset Design and Regression Testing•4 minutes
- Containerizing and Scaling Agents•4 minutes
- Demonstration: Project Architecture and System Overview•5 minutes
- Demonstration: Environment Setup and Data Ingestion Pipeline•7 minutes
- Demonstration: Production-grade Resume Analysis Workflow•6 minutes
- Demonstration: End-to-End Application Orchestration with Streamlit•6 minutes
- Demonstration: Streamlit Interface for AI Resume Screening•6 minutes
- Demonstration: LangSmith Observability and Performance Analysis•5 minutes
4 readings•Total 60 minutes
- Designing Production-Ready Agent APIs with Execution Visibility•15 minutes
- Containerizing Production Agent Systems with Docker•15 minutes
- Bridging Model Pipelines, Observability, and Deployment•15 minutes
- Module Summary: Deploying, Observing, and Scaling Production Agent Systems•15 minutes
4 assignments•Total 33 minutes
- Knowledge Check: Deploying, Observing, and Scaling Production Agent Systems•15 minutes
- Practice Assignment: Production Agent APIs and Execution Visibility•6 minutes
- Practice Assignement: Observability, Evaluation, and Quality Monitoring with LangSmith•6 minutes
- Practice Assignement: End-to-End Production Application Deployment•6 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 video•Total 3 minutes
- Course Summary•3 minutes
1 reading•Total 30 minutes
- Practice Project: Designing an MCP-Powered Intelligent Incident Response Agent•30 minutes
2 assignments•Total 60 minutes
- End Course Knowledge Check: Developing MCP-Powered Agentic AI Systems•30 minutes
- Building an MCP-Powered Agent System for Enterprise Document Intelligence•30 minutes
1 discussion prompt•Total 5 minutes
- Describe Your Learning Journey•5 minutes
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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.
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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.
