Production AI Agents with LangChain + LangGraph [2026]

<div>Stop building AI demos. Start shipping AI agents that handle real workloads in production.</div><div><br></div><div>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.</div><div><br></div><div>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.</div><div><br></div><div>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.</div><div><br></div><div>All code uses the latest stable APIs as of January 2026.</div><div><br></div><div>What you will build:</div><div><ul><li><span style="font-size: 1rem;">Customer Support Agent: RAG-powered knowledge base with Chroma, structured issue classification, automatic ticket escalation. Target: reduce Tier-1 support tickets by 40 percent.</span></li><li><span style="font-size: 1rem;">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.</span></li><li><span style="font-size: 1rem;">Production FastAPI + LangGraph API: Full request pipeline with security middleware, response caching, rate limiting, structured logging, metrics, LangSmith tracing, and Docker deployment to Render</span></li></ul></div><div><br></div><div><span style="font-size: 1rem;">What you will learn:</span></div><div><ul><li><span style="font-size: 1rem;">LangChain v0.3 Mastery: LCEL chain composition, structured output with Pydantic, multi-provider LLM switching (OpenAI, Anthropic, HuggingFace), streaming, and batch processing</span></li><li><span style="font-size: 1rem;">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</span></li><li><span style="font-size: 1rem;">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</span></li><li><span style="font-size: 1rem;">Multi-Agent Orchestration: Supervisor pattern, agent handoffs, parallel execution with fan-out and fan-in, inter-agent communication, and hierarchical team structures</span></li><li><span style="font-size: 1rem;">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</span></li><li><span style="font-size: 1rem;">LLM Testing and Evaluation: Unit tests with mocks, integration tests, regression tests, AB prompt testing, and semantic scoring across correctness, relevance, coherence, and helpfulness</span></li><li><span style="font-size: 1rem;">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</span></li></ul></div><div><br></div><div>How this course is different:</div><div><br></div><div>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.</div><div><br></div><div><span style="font-size: 1rem;">This course is for you if:</span></div><div><ul><li><span style="font-size: 1rem;">You are a Python developer who wants to add AI agent engineering skills to your toolkit</span></li><li><span style="font-size: 1rem;">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</span></li><li><span style="font-size: 1rem;">You are a backend or full-stack developer who wants to integrate AI agents into existing products and APIs</span></li><li><span style="font-size: 1rem;">You are targeting the AI engineer role and need a portfolio of deployed, real-world projects to show employers</span></li></ul></div><div><br></div><div><span style="font-size: 1rem;">Requirements:</span></div><div><ul><li><span style="font-size: 1rem;">Python at an intermediate level (functions, classes, decorators, type hints)</span></li><li><span style="font-size: 1rem;">Basic command line familiarity</span></li><li><span style="font-size: 1rem;">An OpenAI API key (costs roughly $2 to $5 for the entire course)</span></li><li><span style="font-size: 1rem;">No prior LangChain or LangGraph experience required</span></li></ul></div><div><br></div><div><span style="font-size: 1rem;">About the instructor:</span></div><div><br></div><div>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.</div>

What you'll learn:

• Build composable LLM chains using LangChain v.1's LCEL with structured output, streaming, batch processing, and multi-provider switching
• Implement production RAG pipelines with intelligent chunking, vector stores, and 4 advanced retrieval patterns: Multi-Query, Contextual Compression, Hybrid Sear
• Design stateful AI agents with LangGraph state machines, conditional routing, self-correcting loops, and human-in-the-loop approval workflows
• Orchestrate multi-agent systems using supervisor patterns, agent handoffs, parallel execution, and hierarchical team structures
• Secure LLM applications against prompt injection, PII leakage, and output manipulation with production-grade defense layers
• Test and evaluate LLM systems using unit tests, integration tests, and semantic evaluation across correctness, relevance, and coherence
• Deploy production APIs with FastAPI, rate limiting, response caching, structured logging, metrics, LangSmith tracing, and Docker
• Build 3 real-world applications: Customer Support Agent, Multi-Agent Research System, and Code Review Agent, each with measurable business ROI