I Put JEV (TypeSafe's New LLM Router) to work on AWS and fell into a Confidence Trap

I put JEV — the new LLM router from TypeSafe AI — into a real production app running on AWS, and tested whether it lives up to the hype. In this video I break down what JEV actually is, show the code, and watch it route live requests between a quick model and a LangChain DeepAgent in my Resume Generator Pro application. Plus: the mistake I made that sent almost every request down the expensive path, and the confidence-score trap you should avoid.⏱️ Chapters 0:00 Intro — what the hype is about 1:30 JEV explained: a classifier, not a text generator 2:49 Why everyone's excited — LLM routing 3:33 My production app: Resume Generator Pro on AWS 4:44 Two research paths — DeepAgent vs quick search 6:00 A textbook routing problem 6:13 The code — how JEV is integrated 6:35 One call, six questions (how it's wired up) 7:59 Live demo #1 — quick path (Grok Beta @ 100%) 8:57 Live demo #2 — deep research path (GPT-4o Mini) 10:44 The mistake that routed everything the wrong way 11:57 The confidence-score trap 13:00 Wrap-up + code on GitHub 💡 What you'll learn What JEV is and why it beats a native LLM for classification How to use JEV for LLM routing with typed answers + confidence scores A real LangChain DeepAgent vs one-search decision, live in AWS Why routing criteria need checkable triggers, not a vibe prompt Why you should not use JEV's confidence score as a threshold Resume Generator Pro is built with Next.js + FastAPI and hosted on AWS. The code is on GitHub — link in comments. If you're trying JEV in your own projects, I'm happy to share and learn from your implementations too.#jevel #LLMRouting #LangChain #AIAgents #AWS #DeepAgent #AI #SoftwareEngineering