Evidence brief
Development teams can understand how evaluation, harness, environments, and feedback loops affect model system performance.
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.
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Source attributionX · @LangChain
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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%.
Why it mattersThis feature productizes production Agent trajectory evaluation, potentially reducing ongoing evaluation costs for teams.
Original source@colifran_ said users who want to learn more about openwiki can now view the official langchain documentation.
Why it mattersThe documentation can lower the barrier for developers to understand and integrate openwiki and LangChain capabilities.
Original source@sydneyrunkle said @LangChain is hiring open source devs and is looking for people interested in building products at the frontier of agents.
Why it mattersHiring focus shows LangChain continues to invest in Agent-related open-source development.
Original sourceLangChain, @awscloud, and @MongoDB will host AWS Agentic AI Partner Showcase Extended in San Francisco, showcasing the build process for production-ready agents from code to deployment.
Why it mattersDevelopers can see the build, demo, and deployment stages for production-grade Agents.
Original sourceIn 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