LangChain, LangGraph, LangSmith, and Deep Agents

What You Will Learn

This book answers the questions that come up in senior AI engineering interviews:

How LangChain's message layer works and why it matters for multimodal content

How to build reliable agents with middleware, HITL approvals, PII guards, and retries

How to design advanced RAG pipelines combining sparse BM25, dense embeddings, and hybrid reranking

How LangGraph state machines work and how to checkpoint, replay, and time-travel through execution

How to build a deep-agent harness with planning, subagent orchestration, and context offloading

How to secure agents with guardrails, permission gates, and trajectory-level evaluation

How to extend agents over a network using the Model Context Protocol (MCP)

How to give agents a shell safely inside sandboxed execution backends

How to industrialise evaluation with agentevals and LangSmith CI gates