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Evidence brief

LangSmith Tuned Evaluators automatically scores Agent behavior

This feature productizes production Agent trajectory evaluation, potentially reducing ongoing evaluation costs for teams.

Published
Updated
Editorial
Frontline Lab
Source
X
Source author
@LangChain
Related topics
1
Collected
2026-08-19

Frontline Lab summary and source

Editorial summary

LangSmith launched Tuned Evaluators to automatically score agent behavior in production, with Perceived Error as the first metric; it says the specialized model outperformed tested frontier models in benchmarks and cut evaluation costs by 82%.

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Source attributionX · @LangChain

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Related published evidence

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LangChain roundtable discusses Automating Eval and environment engineering

LangChain announced an Automating Eval & Environment Engineering livestream roundtable with participants from LangChain, Prime Intellect, and Baseten, covering improvement loops, open models, and model-harness co-design.

Why it mattersDevelopment teams can understand how evaluation, harness, environments, and feedback loops affect model system performance.

Original source
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Perceived Error is used to measure Agent user experience

In the repost, @jakebroekhuizen says Perceived Error is one of the clear signals for whether an Agent delivers a good user experience, and says its model has been tuned for this metric.

Why it mattersAgent teams can use user-perceived errors as signals for experience optimization and model tuning.

Original source