AI Briefing#langsmith
The latest AI and developer news about #langsmith, with the original source and a short summary.
The latest AI and developer news about #langsmith, with the original source and a short summary.
LangSmith Fleet has integrated with Arcade.dev, enabling secure use of 7,500+ tools.
Right after deployment, the system detects and judges regressions, and an agent automatically fixes them via a PR.
A roundup of March updates including LangSmith Fleet, Polly GA, NVIDIA integration, and Open SWE.
An AI agent stack combining vector search, state persistence, Text-to-MQL, and observability on top of MongoDB Atlas has arrived.
It provides a key checklist of steps to take before building evaluation infrastructure in order to successfully build AI agents.
It presents a step-by-step checklist for effectively evaluating AI Agent performance and deploying it reliably.
It covers strategies for designing and managing Targeted Evals to accurately measure an agent's actual behavior in production.
To improve an agent's accuracy and reliability, you need to build targeted evaluation systems that reflect real production behavior.
LangSmith Fleet has launched a 'Skills' feature that gives agents domain knowledge and allows it to be shared within teams.
Moda used Deep Agents and LangSmith to build a production-grade multi-agent system that performs complex visual design.
LangChain is participating in Google Cloud Next 2026, unveiling the latest technology for agent development and LangSmith updates.
LangSmith has officially launched **Polly**, an AI assistant that supports trace analysis and prompt editing across all work environments.
LangChain built a GTM agent based on Deep Agents to automate its sales process, increasing lead conversion rates by 250%.
To effectively measure the performance of AI agents, an iterative evaluation process is needed that defines tasks, sets performance metrics, and leverages LangSmith.
monday Service leveraged LangSmith to build a code-centric evaluation framework that verifies agent quality from the early stages of development.
As LLMs advance, agent frameworks are evolving from simple chaining to autonomous workflow orchestration.
LangChain unveiled its major January updates, including the GA of LangSmith Agent Builder and a new experiment comparison feature.
LangChain has unveiled 5 core evaluation patterns for effectively validating Deep Agents.
LangChain has launched LangSmith Agent Builder, which allows not only developers but also general business users to easily build agents.
Monte Carlo built an AI agent using LangGraph that investigates and resolves data pipeline issues in parallel.
LangSmith now supports OpenTelemetry, standardizing tracing for LLM applications and providing unified observability.
LangChain has released its annual report covering 2024 AI development trends and LLM infrastructure usage.
LangSmith now supports the OpenTelemetry format, strengthening its distributed tracing and observability capabilities.
The LangChain team has released the Promptim library, which automatically improves prompts based on datasets and evaluation metrics.