Do all of your agents need a frontier model? We ran NVIDIA's open source router,
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- eval — A repeatable test for AI quality — a set of tasks plus scoring — used the way software teams use test suites, because model output is too variable to judge by eyeballing.
- AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
It puts a number on how much of an 's work actually needs a frontier model, and the integration is available now rather than described in the abstract.
“Stage routing is signal-driven. It can route ordinary turns to the efficient target and escalate turns with critical failed-tool signals to the capable target without making a separate judge-model call.”
LangChain
“The profile adds model-specific prompt guidance, tool descriptions, and middleware for tool calling, filesystem operations, retries, context management, and final answers. It was developed through evaluation-driven harness tuning .”
LangChain
“The profile is registered for Nemotron 3 Ultra model identifiers served through NVIDIA, Baseten, Fireworks, OpenRouter, Nebius, and Together. Registration is model-specific, so other models from these providers remain unchanged.”
LangChain
“The order of the targets passed to stage_router matters: pass the capable target first and the efficient target second.”
LangChain
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