Evidence brief
Agent teams can use user-perceived errors as signals for experience optimization and model tuning.
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.
This brief preserves the original source so the summary and editorial context can be checked independently.
Source attributionX · @LangChain
Open the original sourceRelationships are derived from shared topics, entities, categories, tags, and community context; every result remains independently source-linked.
LangChain, @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 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 sourceLangSmith 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@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 sourceTesla alum @DrewBaglino explains on the show how Heron Power is rebuilding grid infrastructure to ease AI power bottlenecks; the article also says he has raised $140 million for this.
Why it mattersData center power supply efficiency directly affects available AI compute capacity and operating costs.
Original source