Learn the engineering skills behind one of software’s most valuable emerging fields
AI engineering is rapidly becoming one of the most commercially valuable areas of software development.
Companies are moving beyond simple chatbot prototypes and looking for engineers who can build reliable AI-powered applications, agentic workflows, retrieval systems, model integrations, and production infrastructure. That creates strong demand for developers who understand both software engineering fundamentals and modern AI application frameworks.
Python sits at the center of this ecosystem. It is the dominant language across AI, machine learning, data engineering, model tooling, and an increasing share of agent development. LangChain and LangGraph add the abstractions needed to build applications around large language models, tools, retrieval, state, and increasingly autonomous workflows.
These skills also occupy an interesting position in the changing software market. AI can automate more routine implementation work, but organizations still need engineers who can design the systems around AI: integration, orchestration, reliability, evaluation, security, deployment, observability, and production control. Learning to build the systems that use AI is therefore a compelling way to move toward the part of software engineering that AI itself is helping to create.
Production-Grade Python, LangChain & LangGraph is designed to take you there.
The book assumes that you already know basic Python syntax. Instead of teaching loops, functions, and classes again, it focuses on the engineering knowledge required to turn Python code into real production software.
You will learn:
- How Python actually executes code through CPython, bytecode, modules, and processes
- Modern project structure with
pyproject.toml - Virtual environments, dependency management, locking, packaging, wheels, and reproducible builds
- FastAPI and production API design
- Configuration and secrets management
- Async programming and concurrency
- Database access, transactions, migrations, and persistence
- Caching and external service integration
- Error handling, retries, timeouts, and idempotency
- Structured logging, metrics, tracing, and health checks
- Unit, integration, API, and system testing
- Containers, CI/CD, and production deployment
- Security practices for backend and AI applications
The second half applies those engineering foundations to modern AI engineering with LangChain and LangGraph.
You will learn how to build:
- LLM-powered applications
- Structured model interactions
- Tool-using agents
- Retrieval-Augmented Generation systems
- Stateful workflows
- Conditional and parallel execution
- Persistent agent state
- Durable workflows
- Human-in-the-loop approval
- Streaming applications
- Long-running agent processes
- Multi-step and multi-agent systems
- Testable and observable AI workflows
The emphasis throughout is on production engineering, not demos.
Calling an LLM is easy. Building a system around it that remains secure, reliable, observable, recoverable, and maintainable is where much of the real engineering value lies.
A complete production-style support-agent application brings the concepts together using FastAPI, SQLAlchemy, LangChain, LangGraph, persistence, authentication, observability, human approval, and safe execution of irreversible actions.
The book also includes a companion code repository containing the complete application, runnable examples, tests, deployment configuration, and every code sample used throughout the manuscript.
If you already know basic Python and want to position yourself for the growing field of AI engineering, this book gives you the production foundations and agent-development skills needed to move beyond prototypes and build systems organizations can actually depend on.
