Three LangGraph Agent Patterns That Replaced Hundreds of Lines of Glue Code

What if your AI system's biggest problem isn't the AI? I've watched teams spend months fine-tuning prompts, swapping models, and chasing benchmark improvements — only to realize their actual bottleneck was architecture . The model was fine. The way they wired it together was the problem. After building production multi-agent systems with LangGraph and LangChain across financial analysis, document processing, and operational automation, I've converged on three reusable agent patterns that handle the vast majority of agentic workflows. They're not novel research. They won't trend on AI Twitter. But they quietly eliminated entire categories of bugs, cut development time on new pipelines by half, and — most importantly — made the systems predictable enough that non-AI engineers on the team could reason about them. This article walks through each pattern with simplified code samples and practical examples. Whether you're a CTO evaluating agentic architectures or an engineer knee-deep in Lan