MCP Servers & Agentic AI Architecture

Overview

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Imagine building an AI that doesn’t wait for prompts but actively runs your backend, selects the right tools, and completes tasks end-to-end. Most AI applications are limited to generating answers. But real-world systems require structured execution, intelligent workflows, and scalable architecture and that’s where most developers fall behind. In this course, you will build MCP-based AI systems that go beyond responses. You will implement backend logic using services and controllers, build MCP servers, and define tools, resources, and prompts to enable AI to execute in a controlled manner. Get hands-on experience with tool deployments using Gemini and OpenAI, request-response cycles, and agent controllers that cover autonomous workflows. You will also work with vector databases such as ChromaDB and pgVector to enhance context retrieval, accelerate data ingestion, and produce intelligent AI outputs. This Agentic AI course is designed for developers looking to level up and build agent-powered, production-ready systems for real-world industry needs. Stop building AI that just responds start building AI that operates. Enroll now and lead the next wave of intelligent systems.

Syllabus

MCP Backend Architecture and AI Tool Fundamentals

Learn the MCP server architecture, including tools, resources, prompts, transport layers, and session management. Implement customer and order services to build a strong backend foundation.

AI Tool Calling and LLM Integration

Integrate Gemini and OpenAI tools, implement function calling workflows, and understand why MCP improves tool execution over direct calls. Test and debug AI tool calls for real-world scenarios.

MCP Server Client and Agent Integration

Connect MCP server and client, implement multiple tools, and integrate with LLMs to build agentic workflows. Learn session management, transport logic, and automated tool execution.

MCP Integration with AI Models

Integrate MCP with AI models, implement the agentic loop, parse responses, and handle outputs effectively. Build production-ready agentic workflows and optimize system instructions.

Vector Database Concepts

Enable AI memory and retrieval using vector databases like ChromaDB and pgVector. Implement document ingestion, embeddings, and query pipelines for context-aware AI systems.