Building Production AI Agents with LangGraph: Beyond the Toy Examples

Building Production AI Agents with LangGraph: Beyond the Toy Examples Every AI tutorial shows you a chatbot that answers questions. That's not an agent. An agent decides what to do, takes action, observes the result, and adapts. In production, it does all of that reliably, with audit trails, error recovery, and human oversight. LangGraph — the graph-based orchestration layer from LangChain — has quietly become the framework of choice for teams shipping real agents. Uber routes support workflows through it. LinkedIn uses it for internal knowledge agents. Klarna runs customer-facing agents on it at scale. This article is the guide I wish I had when I moved from prototype to production. We'll build a Research Assistant agent end-to-end, covering every pattern that matters when uptime counts. When to Use Agents (and When Not To) Before writing a single line of agent code, ask yourself: does this task require dynamic decision-making? Use agents when: The number of steps is unknown at design