Research Explainer · XiaoHu Explains

LangChain: Tuning the Harness, Not the Model — Nemotron 3 Ultra Closes In on Opus 4.8 at About 1/10 the Cost, Proving Enterprises Don't Need to Pay Top Dollar for Closed APIs

Three levers: system prompt, tool descriptions, middleware. On the Deep Agents suite, typical scores go ~0.80→0.84, with a best run of 0.86 versus Opus's 0.87.
⚑ Stance note: this piece is a methodology case study from LangChain's official blog. The data comes from its internal Deep Agents evals and its own trace analysis — this is not a third-party reproduction.
1WHAT

What They Did

LangChain published a harness-tuning playbook for Nemotron 3 Ultra: leave the model untouched, change only the scaffolding around it, and see how high an open model can be lifted on agent tasks.

Primary source and reference