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 de