Building AI Agents with LangGraph and Agent Frameworks

About this course

This course contains the use of artificial intelligence.

Most AI demos look impressive for five minutes. A chatbot answers a question, calls a tool, and everyone applauds. But what happens when a real business process needs ten steps, two different systems, a human approval, and the ability to recover when something fails?

That's where LangGraph comes in.

In Building AI Agents with LangGraph and Agent Frameworks, you'll learn how to design AI applications that do more than generate responses. You'll build stateful agents that follow workflows, make routing decisions, call tools, retain context, collaborate with other agents, and keep people in control of important actions.

We'll start with the foundations: how LangGraph represents workflows using nodes, edges, and shared state. Then you'll create conditional paths, loops, and multi-step processes using Python. You'll connect language models through LangChain and Ollama, giving you a practical way to complete the core exercises without paying for an AI API subscription.

Instead of jumping between unrelated examples, we'll build one project throughout the course: BrightDesk, a fictional enterprise customer support platform. It begins as a simple ticket router and gradually becomes a multi-agent system with specialized support, billing, and knowledge agents.

As BrightDesk grows, you'll learn to integrate REST APIs, custom tools, databases, and retrieval-augmented generation (RAG). You'll implement conversation memory, persistent checkpoints, and workflow recovery so your agents can handle more than a single request at a time. You'll also create human-in-the-loop approval steps for sensitive actions, such as refunds or account changes.

Of course, building an agent is only half the challenge. Making it reliable is where the real engineering begins. We'll explore testing, evaluation, structured logging, monitoring, error handling, retries, security guardrails, and permission controls.

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