AI Agent Orchestration: LangGraph, Temporal & Custom Workflows (2026 Guide)

<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

<h1> LangGraph State Machine Patterns — How to Stop AI Agents from Going Off-Script </h1> <h2> The "Agent Does Something Unexpected" Problem </h2> <p>LLM-powered agents that run

<p>Claude's tool use (function calling) API is what separates toy chatbots from actual agents. I've built production agents with it — here's what reliable tool use looks like when

<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>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>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

<p>Most developers who build AI agents make the same mistake: they spend weeks designing the orchestration layer, tuning the system prompt, and picking the right model — then hand

<p>You've heard of DevOps. You've heard of AIOps. You've probably heard of MLOps.</p> <p>But there's a category that doesn't exist yet — and it's the one the AI industry needs