Deploying AI Agents with LangGraph and Production APIs

Moving AI prototypes from a local script to a reliable production environment is one of the biggest challenges in modern software development. This text-based course guides you through the foundational concepts and practical steps required to build, secure, and host robust AI agents. You will transition from writing simple prompts to deploying stateful, multi-agent workflows. By understanding the underlying architecture of Large Language Models (LLMs) and graph-based agent orchestration, you will gain the skills to expose your AI systems as secure, high-performance APIs. What you'll learn: - Understand the fundamental terminology of AI agents, state machines, and LLM orchestration. - Build stateful, multi-agent workflows using LangGraph and Python. - Configure production-ready APIs using FastAPI with modern async/await patterns. - Manage dependencies and virtual environments securely using modern packaging standards. - Implement basic monitoring and observability to track agent decisions and API performance. - Deploy your agentic applications to cloud environments using container-ready configurations. The course begins with foundational definitions of agentic systems and graph architectures before moving into step-by-step implementation. You will read through detailed code examples, conceptual breakdowns, and deployment configurations designed to make your transition to production seamless. This course is designed for beginners and software developers who want to deploy their first agentic application, with no prior LangGraph experience required. Start reading today to bridge the gap between AI concepts and production reality.