My First Agentic AI With Model Context Protocol (Full Demo + Code Explained)-Think-Act-Memory-Recall
Have you ever wondered how to build AI agents that are scalable, secure, and maintainable? In this comprehensive tutorial, we move beyond simple, monolithic Agentic AI Codebase and show you the professional way to architect agentic AI systems using the Model Context Protocol (MCP).
We'll start with a simple agent that has all its tools in one file and then completely refactor it, decoupling the tools into their own independent MCP servers. You'll see firsthand how the agent thinks, acts, and uses the MCP client-server model to interact with external tools like a calculator and a file system for any requested task autonomously, all explained with a live demo and a line-by-line code walkthrough.
If you're ready to level up your AI development skills and build robust, production-ready agents, this video is for you!
In this video, We will cover -
- Introduction: The Problem with Monolithic Agents
- Demo: Agentic AI with MCP in Action
- The Core Concept: Think, Act, Observe Loop
- How MCP Works: How tasks divide into small small activities to autonomously accomplish final response
- Code Walkthrough: The Monolithic Agent (Before MCP)
- Refactoring with MCP: How to building the Tool Servers & Client in agent by using StdioServerParameters and ClientSession
- Code Walkthrough: The Decoupled Agent (After MCP)
- Final Result & Analysis
- Why This Architecture is Better (Monolithic vs. MCP)
- MCP Transports Explained (stdio, Streamable HTTP, Server-Sent Events)
- Final Conclusion on how MCP based agentic AI achieves Modularity, Scalability, Security, and Reusability.
If you want to build AI agents that are ready for real-world applications, this architectural guide is for you.
If this video helped you understand Agentic AI architecture, please like, subscribe, and let me know in the comments what tools you'd build next!
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