Production AI Agents with LangChain + LangGraph [2026]

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 Retriever

• LangGraph Deep Dive (4 hours): State machines with TypedDict, conditional routing, self-correcting loops, human-in-the-loop workflows with interrupt patterns, and checkpoint persistence

• Multi-Agent Orchestration: Supervisor pattern, agent handoffs, parallel execution with fan-out and fan-in, inter-agent communication, and hierarchical team structures

• Production Security: Prompt injection defense with regex patterns, PII detection and masking for emails, SSNs and credit cards, LLM-as-Guard pattern, and output validation

• LLM Testing and Evaluation: Unit tests with mocks, integration tests, regression tests, AB prompt testing, and semantic scoring across correctness, relevance, coherence, and helpfulness

• Production Deployment: FastAPI integration, rate limiting, response caching with SHA-256 hashing and TTL, structured JSON logging, metrics collection, LangSmith tracing, Docker, and cloud deployment to Render

How this course is different:

Most AI courses stop at hello world demos. This course is production-first from day one. Every concept is taught through working, deployable code. Security and testing are dedicated modules, not afterthoughts. You will implement error handling, fallbacks, cost optimization, and monitoring throughout. The final API project wires everything together into a system you can actually ship.

This course is for you if:

• You are a Python developer who wants to add AI agent engineering skills to your toolkit

• You have done LangChain tutorials and can call an LLM, but do not know how to build something that handles errors, scales, and stays stable in production

• You are a backend or full-stack developer who wants to integrate AI agents into existing products and APIs

• You are targeting the AI engineer role and need a portfolio of deployed, real-world projects to show employers

Requirements:

• Python at an intermediate level (functions, classes, decorators, type hints)

• Basic command line familiarity

• An OpenAI API key (costs roughly $2 to $5 for the entire course)

• No prior LangChain or LangGraph experience required

About the instructor:

Paulo Dichone is an AI engineer and educator with over 340,000 students across 71 courses. Every pattern in this course comes from real production systems. You will get the same battle-tested approaches, shortcuts, and lessons learned from building AI applications that run in the real world.