Developing MCP-Powered Agentic AI Systems

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 r