@LangChain: Head of AI @nlarusstone on the patterns @benchling uses to look at production traces.
Summary
Head of AI at Benchling discusses patterns for analyzing production traces in a tech talk.
View Cached Full Text
Cached at: 06/12/26, 05:00 PM
Head of AI @nlarusstone on the patterns @benchling uses to look at production traces. https://t.co/0IKvAtAeZi
Similar Articles
@LangChain: .@AdamRLucek on how we use traces to build evals for production agents.
Adam Łucek discusses how LangChain uses trace data to build evaluations for production agents.
@LangChain: ICYMI: At @aiDotEngineer World’s Fair, @vtrivedy10 took the stage to share why data mining from traces is one of the hi…
At the aiDotEngineer World's Fair, Vtrivedy10 discussed how data mining from traces is a high-leverage practice for understanding AI agents, curating data at scale, and running improvement loops.
Building advanced AI workflows—what am I missing?
A developer seeking recommendations on advanced AI workflow orchestration tools and patterns, including LangChain, LangGraph, and AWS Step Functions, to build more robust and future-proof systems.
@rajeevchhajer: Key ideas I picked up at the @LangChain conference this week: "AI engineering is data science. Look at your data." — @s…
Key takeaways from the LangChain conference, including insights on AI engineering as data science, the priority of data strategy before agents, coding agents as the new substrate, and context plus memory as the new moat.
@LangChain: Improving agents The old way: Manually reading traces, looking for patterns, writing evals, and creating fixes. The bet…
This tweet contrasts the old manual approach to improving AI agents with a new automated method using LangSmith Engine, which cycles through tracing, eval, and fixes.