Agent loops use a full-size LLM call every time, even for

@cv_usk cv usk · Sep 19, 2026 · case #624801 · listed via Jev Hub

Still from the clip posted with this case; watch it in the original post on X.

Wrote a LangChain blog post titled 'Building a Harness with Jev' explaining how Jev replaces full LLM calls for simple yes/no agent loop decisions.

The original post, translated

Agent loops use a full-size LLM call every time, even for simple yes/no decisions, so there's actually a lot of waste. I'll introduce a new way to use models that solves this. Title: Building a Harness with Jev URL: https:// langchain.com/blog/building- a-harness-with-jev … ❓ What's new about Jev? 💡 TypeSafe

Show the original post in its source language ▾

エージェントのループって、単純なyes/no判定ですら毎回フルサイズのLLM呼び出しを使っていて、実は無駄が多いんです。それを解決する新しいモデルの使い方を紹介します。タイトル: Building a Harness with Jev URL: https:// langchain.com/blog/building- a-harness-with-jev … ❓ Jevって何が新しいの? 💡 TypeSafe

The post above is a machine translation from ja; the untranslated text is in the fold-out.

Engagement when collected

Likes0

Bookmarks0

Reposts0

Replies0

Quotes0

Numbers are a snapshot taken from X when the case was added to the library (schema v1, collected 2026-09-19); they will not match today.

Where this case fits

Filed under agents & workflows. In the pattern Jev is built for, the model answers a bounded question per step — and ordinary code acts on the answer, because the answer is already a value rather than a paragraph. Other posts in the same family are on the agents & workflows page.

🤖 Agents & workflows