LangGraph State Machine Patterns — How to Stop AI Agents from Going Off-Script

<p>A single AI agent with a few tools can handle simple tasks. But real-world workflows — process a customer refund, generate a research report, onboard a new user — involve

<h1> Building Production AI Agents with LangGraph: Beyond the Toy Examples </h1> <p>Every AI tutorial shows you a chatbot that answers questions. That's not an agent. An agent

<p>Most LangGraph content stops at the notebook. You build a cute ReAct loop, it answers one question, and the article ends before the hard part: <em>how do you actually serve

<h1> LangGraph 워크플로우 템플릿 (v39) </h1> <h2> LangGraph 아키텍처 개요 </h2> <p>LangGraph는 상태 기반 워크플로우를 구현하기 위한 프레임워크로, 다음과 같은 핵심 구성 요소로 작동합니다:</p> <p><strong>Nodes (노드)</strong>: 워크플로우의 각

<p>Everyone's building agents. Most are building them wrong. Not because they lack skill, but because they lack the right mental models. Before you write a line of code, you need

<p>When it comes to healthcare, "hallucination" isn't just a quirky AI bug—it's a critical safety risk. Building a system that flags <strong>Drug-Drug Interactions (DDI)</strong>

<p>Managing chronic conditions like Type 1 Diabetes is an exhausting, 24/7 mental load. Patients have to constantly monitor <strong>Continuous Glucose Monitor (CGM)</strong> data,

<p>If you have built a multi-agent AI system over the past two years, you have almost certainly encountered what we call the <strong>Supervisor Tax</strong>.</p> <p>The pattern