AI Agents Bootcamp: LangChain, LangGraph, RAG & MCP [2026]

About this course

This course contains the use of artificial intelligence.

This AI Agents Bootcamp takes you from your first agent loop in plain Python to production AI agents built with LangChain, LangGraph, RAG, MCP and LangSmith, tested with evals and shipped behind FastAPI.You can call a model. Maybe you have followed a tutorial and watched an agent answer a question, call a tool, and loop. Then you tried to put it in front of real tickets and it invented a policy, looped on a tool call, forgot the thread, or paid a refund nobody approved. That gap between a demo and an agentic AI system your team can ship to production is the whole reason this bootcamp exists.

You will build AI agents the way an engineer builds them: a plain Python loop first, so you know exactly what a framework is doing for you, then LangChain 1.x tools and create_agent, then LangGraph for control, memory, human approval and multi-agent teams, then retrieval, evals, MCP (the Model Context Protocol), security, a chat UI and a FastAPI deployment. Every section is a concept lecture that draws the picture, followed by a lab that types the real file and shows the real output. Nothing on screen is staged.

Build, Evaluate and Ship Production AI Agents with LangChain, LangGraph, RAG and MCP

Build an agent loop in plain Python and break it three ways before any framework touches itCall any LLM from one config file: OpenAI, Anthropic, Google, or a free local model in OllamaWrite typed LangChain tools and ship the TechCorp IT desk with create_agent and middleware

Design LangGraph graphs with routing, reducers, a checkpointer, interrupt, streaming and time travel

Run parallel nodes, map-reduce, subgraphs, deep agents and a supervisor with specialist agents

Give an agent retrieval as a tool with FAISS and pgvector, then route, grade, rewrite, cite and refuse

Trace every run in LangSmith, score it against a golden set, and fail regressions

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