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@wonder is building an AI agent to automate meal planning and delivery, integrating LangSmith for debugging and scoring using LLM-as-judge.
LangSmith has launched Custom Apps, now generally available, allowing users to build and publish custom interfaces for their agent data within the platform to enhance workflows like annotation and experiment comparison.
LangChain introduces Trajectories in LangSmith, a chronological view of agent sessions that simplifies debugging by aggregating messages from humans, AI, and tools in order for easier navigation and analysis.
LangChain promotes Agent Claw Play for LangSmith Engine in a tweet, inviting users to try out the interactive development tool.
LangChain announced LangSmith Engine v2 with features like red teaming and validated fixes to proactively identify and resolve issues in AI agents before they affect production.
LangChain announces new features in LangSmith from their keynote, including LangSmith Engine v2 for red teaming, Managed Deep Agents v0.8 with new auth and memory, LangSmith Trajectories for agent session views, and LangSmith Fine-Tuning.
LangChain announces an event on September 30th focused on online evaluations, demonstrating how to use Tuned Evaluators in LangSmith to automatically analyze AI agent interactions and provide feedback in production.
LangSmith now supports decision models such as Jev and SemIf, providing visibility into each step to help debug faster and understand model behavior.
LangSmith is helping healthcare organizations transform expert reviews into reusable evaluations for AI agents, improving patient care workflows.
LangSmith now integrates Jev, a System One model, for fast and cost-effective online evaluations of agent traces, enabling broader scoring and safety checks without high costs.
LangChain announces that SemIf, an open-source decision model, is available for free for one week in the LangSmith LLM Gateway, compatible with the TypeSafe SDK.
Enzo Health uses LangSmith to trace and evaluate its clinical AI pipelines in a HIPAA-compliant healthcare setting, and the company is hiring engineers to advance AI in clinical workflows.
Included Health built Dot, an AI-powered federated multi-agent healthcare guide, using LangGraph and Deep Agents, resulting in a 75% increase in chat engagement and over 99% high-risk detection.
Deep Life Sci is an open source agentic assistant built for clinical and lab scientists, providing access to vast databases like ClinicalTrials.gov and PubMed with sub-agents for efficient document review.
LangSmith has rebuilt its filtering experience for agent traces, making it faster and easier to build queries, find runs, and understand matches.
Clay demonstrates integrating LangSmith tracing into their agent harness built on Vercel AI SDK, requiring only one line of code.
LangChain demonstrates how LangGraph and LangSmith have accelerated support agent development at Lyft, reducing shipping time from six months to 1-2 weeks with enhanced visibility.
LangChain has added a capstone project to their LangSmith Essentials course, providing more opportunities to practice debugging and tracing skills in AI agent engineering.
The article explains evaluation methods for AI agents, covering easy, hard, and advanced modes with practical advice on tools like Harbor and LangSmith for safe testing and self-improvement.
LangSmith LLM Gateway enables organizations to control model access by restricting API keys to specific models and automatically blocking unauthorized calls, demonstrated with Opus 5 and Sonnet 5.