How Mux shipped durable video workflows with their @mux/ai SDK
Not a pilot. Mux shipped durable video workflows into production using Vercel's Workflow DevKit—solving the AI reliability problem that kills half of video processing pipelines.
Why it matters
Mux's @mux/ai SDK demonstrates a practical production pattern for building resilient AI workflows. By leveraging Vercel's Workflow primitives, they've solved a concrete developer pain point (mid-pipeline failures causing wasted API costs) and made durable execution accessible without custom infrastructure—a model other infra companies will copy.
The key facts
10 to knowMux shipped @mux/ai SDK built on Vercel Workflow DevKit
Focus: durable execution for AI video workflows (content moderation, chapter generation, etc.)
Problem solved: network timeouts, rate limits, provider 500s no longer require restart-from-scratch or custom state management
Open-source SDK reduces developer friction for building reliable AI features on video infrastructure
Represents emerging pattern: AI infra + durable execution primitives as core shipping requirement
Mux launched @mux/ai SDK (open-source)
Built on Vercel's Workflow DevKit for durable execution
Solves mid-pipeline failures: timeouts, rate limits, provider errors
Targets developers building AI features on video infrastructure
Focus on state management and resumability without complex infra
Go to the source
Vercel Blogvercel.com
Publisher excerpt: We invited from to share how they shipped durable workflows with their .Dylan JhaveriMux@mux/ai SDK AI workflows have a frustrating habit of failing halfway through. Your content moderation check passes, you're generating video chapters, and then you hit a network timeout, a rate limit, or a random…