Discover how companies like LinkedIn and Uber are building sophisticated AI agents with LangGraph, a revolutionary framework that treats agents as persistent workflows rather than simple conversations.

Cree par des anciens de Columbia University a San Francisco
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Cree par des anciens de Columbia University a San Francisco

**Lena:** You know what's wild? I was just reading about how companies like LinkedIn and Uber are building these incredibly sophisticated AI agents, and they're not using the frameworks you'd expect. They're using something called LangGraph that actually throws out most of the traditional assumptions about how to build AI systems.
**Miles:** Right, and that's such a fascinating shift! Most people think of AI agents as these simple back-and-forth conversations, but LangGraph is built on this idea that real production agents need to be more like... well, think of them as persistent, long-running workflows that can pause, resume, and even bring humans into the loop when needed.
**Lena:** Exactly! It's like the difference between a quick chat and managing a complex project that might take days or weeks to complete. I mean, when your AI agent might run for hours and cost significant money if it fails, you can't just restart from scratch every time something goes wrong.
**Miles:** That's the key insight - LangGraph was designed specifically for production reliability rather than just getting started quickly. So let's break down what makes this graph-based approach so different from traditional agent frameworks.