Production AI Agents with LangChain + LangGraph [2026]

About this course

Stop building AI demos. Start shipping AI agents that handle real workloads in production.

Most LangChain and LangGraph tutorials teach you how to call an LLM and leave you on your own when it is time to build something real. This course picks up where they stop. From Lecture 1, you will build production-ready AI agent systems using the same patterns companies are paying $150K salaries for in 2026.

This is a project-first, production-first course covering LangChain v0.3, LangGraph 1.0, RAG pipelines, multi-agent orchestration, security, testing, LangSmith observability, FastAPI deployment, and Docker. All code uses the latest stable APIs as of January 2026.

What you will build:

Customer Support Agent: RAG-powered knowledge base with Chroma, structured issue classification, automatic ticket escalation. Target: reduce Tier-1 support tickets by 40 percent.

Multi-Agent Research System: Specialist agents running in parallel with state management, convergence patterns, and quality loops. Target: cut research time from 4 hours to 20 minutes.

Production FastAPI + LangGraph API: Full request pipeline with security middleware, response caching, rate limiting, structured logging, metrics, LangSmith tracing, and Docker deployment to Render

What you will learn:

LangChain v0.3 Mastery: LCEL chain composition, structured output with Pydantic, multi-provider LLM switching (OpenAI, Anthropic, HuggingFace), streaming, and batch processing

Complete RAG Pipelines: Document loading, intelligent text splitting, embeddings, vector stores with Chroma, and 4 advanced retrieval patterns: Multi-Query, Contextual Compression, Hybrid Search, and Parent Document RetrieverLangGraph Deep Dive (4 hours): State machines with TypedDict, conditional routing, self-correcting loops, human-in-the-loop workflows with interrupt pattern

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