Hands-On Agentic AI Trading System using MCP & LangChain

About this course

Agentic AI is rapidly transforming how intelligent systems are built — moving beyond simple chatbots to autonomous agents that can reason, act, and interact with real-world systems. In this hands-on course, you’ll learn how to design, build, orchestrate, and monitor production-grade agentic AI trading systems using Model Context Protocol (MCP), LangChain, LangGraph, and LangSmith.

This course goes beyond theory. You’ll build a complete end-to-end trading agent that can interpret natural language queries, fetch real-time market data, compute technical indicators like RSI and Bollinger Bands, execute multi-step workflows, and generate actionable trading insights — all while being fully observable and auditable.

We start by laying a strong foundation in MCP and agentic LLM concepts, explaining how modern agents communicate with tools, APIs, and workflows in a standardized and cloud-agnostic way. You’ll then set up your development environment, configure LLM endpoints (including local models via Ollama), and connect real-time financial data sources.

As the course progresses, you’ll design multiple real-world systems — including a trading analysis agent, an enterprise assistant, and an IoT monitoring system — to understand how MCP-powered architectures scale across domains. You’ll implement an MCP server with financial tools, orchestrate workflows using LangGraph, and integrate intelligent clients using LangChain agents with tool discovery and intent detection.

A key focus of this course is observability and compliance. You’ll integrate LangSmith to trace agent reasoning, monitor tool usage, debug workflows, and ensure transparency — an essential requirement for regulated domains like finance.

Finally, everything comes together in a mini project, where you’ll combine MCP server, LangChain client, LangG

Prices, discounts and availability are set by Udemy. We may earn a commission when you purchase through links on this site.