Search: “harrison-chase--x”

great look into how Rippling built RipplingAI .@Rippling AI runs on Deep Agents and LangSmith. Here’s how they shipped to millions of users in 6 months. https://www.langchain.com/blog/how-rippling-wen

great overview of deepagents! what it is, how we made it great at complex tasks, how to take it to production here's a quick overview of a) what is deepagents b) what makes deepagents good at complex

have manually read 1000s traces since joining LangChain! it’s a great way to learn and understand your agent but completely infeasible to do at agent scale 🙃 engine helps us automate that process so h

DeepAgents are AI systems designed to handle complex tasks through advanced reasoning and planning capabilities. The post outlines the key features that enable DeepAgents to excel at complicated probl

LangSmith is a development platform designed to build, test, and monitor LLM applications, offering tools for debugging and evaluation to improve AI application quality and reliability. The statement

This post likely discusses how the LangChain framework (which Harrison Chase co-founded) can assist developers in constructing more effective AI agents by providing tools, abstractions, and patterns f

Managed Deep Agents keeps the project shape you already know: ↳ AGENTS.md, skills/, subagents/, + tools.json Context Hub gives your agent a managed place to retain and update this context across sessi

.@MukilLoganathan’s Interrupt keynote on Sandboxes. https://youtu.be/IIchUA5T3gs In 20 minutes, you’ll learn how to run agent code safely. Isolated from your runtime, with network controls, persistent

Open models! Introducing MiniMax M3: The First Open-Weights Model to Combine Three Frontier Capabilities - Coding & Agentic Frontier: 59.0% SWE-Bench Pro, 66.0% Terminal Bench 2.1, 34.8% SWE-fficiency

.@Rippling AI runs on Deep Agents and LangSmith. Here’s how they shipped to millions of users in 6 months. https://www.langchain.com/blog/how-rippling-went-ai-native-across-every-product-in-6-months-w

I cannot provide an accurate summary of this post as the tweet content itself is not provided, only a reference indicating Jay Farei's engagement with an idea shared by Harrison Chase. Without access

LangSmith Engine is a tool designed to automatically diagnose and resolve agent failures, eliminating the need for manual troubleshooting and triage. The feature appears to leverage automated analysis

This is great @hwchase17 @bryonkuchML Seeing this update, I’m building a tutorial repo around LangChain + Groq + GEPA. The idea is simple: •LangChain builds the RAG/agent workflow •Groq gives fast inf

This macroeconomic research agent powered by Deep Agents, LangSmith, and the @youdotcom Finance Research API: ✅ Analyzes GDP data ✅ Detects anomalies ✅ Investigates structural & cyclical drivers at th

🧑‍⚖️Evaluating Deep Agents with LangSmith on AWS Great deep dive blog with our friends at AWS on evaluating DeepAgents with LangSmith Covers datapoint and evaluator design for longer horizon agents ht

This article from LangChain explores the architectural components and design principles of agent harnesses, which are systems that manage the execution and behavior of AI agents. The piece likely cove

LangChain🤝GEPA shout out to @bryonkuchML for contributing a PR to the GEPA repo to make it work for LangChain! You can now optimize your LangChain chains Docs: https://gepa-ai.github.io/gepa/tutorials

the market is speaking The latest finding in the LangSmith Signal: Open Models are having a moment. 1 in 3 AI teams ran an open-weights model in April 2026, up from 1 in 5 nine months ago. The overall

agent builder! Start creating agents using everyday language with LangSmith Fleet. Learn how to build no-code agents for real work. Take our free LangChain Academy course today: https://academy.langch

Deep Agents v0.6 makes harness profiles a first-class abstraction. Now, you can get production-grade performance from models like @Kimi_Moonshot, @Alibaba_Qwen, and @DeepSeek_ai at 20x+ lower cost tha

Different models need different prompts, sometimes tools “Harness profiles” are how we do that in deepagents Deep Agents v0.6 makes harness profiles a first-class abstraction. Now, you can get product

LangSmith Sandboxes include an automatic pause feature that activates when the environment is idle, preventing users from incurring costs for unused resources. This functionality helps optimize cloud

feel very aligned with our vision & Ronak + the awesome Trajectory team’s on practically tackling Continual Learning at scale 🚀 there’s a very good reason why teams are partly building “Observability

I cannot provide an accurate summary because the URL appears to be invalid or the tweet is no longer accessible (the status ID seems implausible for the current date). To create a reliable knowledge b

I watched the LangChain keynote expecting product releases. Got something better — the clearest breakdown I've heard of why most agents never make it past demo. One thesis lands hard. Here's what sepa

This post from LangChain's Harrison Chase contrasts traditional manual methods of improving AI agents (tracing execution, identifying patterns, writing evaluations, and implementing fixes) with a more

LangSmith LLM Gateway is a feature that enables proactive cost and privacy controls by enforcing spending limits and redacting personally identifiable information (PII) before API requests are sent to

llm spend is starting to get really high... a key part of our LLM gateway is spend visibility and spend control sign up for the private beta to try it out today! https://www.langchain.com/langsmith-ll

open models are having a moment! The latest finding in the LangSmith Signal: Open Models are having a moment. 1 in 3 AI teams ran an open-weights model in April 2026, up from 1 in 5 nine months ago. T

open models will become a huge force for coding. The latest finding in the LangSmith Signal: Open Models are having a moment. 1 in 3 AI teams ran an open-weights model in April 2026, up from 1 in 5 ni

Production traffic from frontier models is a golden data asset. If you can efficiently mine the traces, filter for quality, and fine-tune smaller models on them, you get specialized performance at a f

Start creating agents using everyday language with LangSmith Fleet. Learn how to build no-code agents for real work. Take our free LangChain Academy course today: https://academy.langchain.com/courses

The latest finding in the LangSmith Signal: Open Models are having a moment. 1 in 3 AI teams ran an open-weights model in April 2026, up from 1 in 5 nine months ago. The overall number of teams using

There are still a few spots left for @hwchase17 + @traversal_ai founder @_anish_agarwal's technical fireside chat in NYC on June 2nd. Learn how Traversal builds, ships and improves their agents. Enjoy

Tonight, I'm having dinner with a bunch of high-performing @Rippling team members (something we do every year). So I asked Rippling AI to give me a dossier on everyone I'm seated with, and the quality

a hot (cold at this point?) take that lead us to build this: every agent in the future will need a sandbox to connect to writing/executing code is not just for coding agents! is useful for all sorts o

A TLDR on LangSmith Sandboxes: ✅ Hardware-virtualized microVM ✅ Kernel-isolated from your services + other sandboxes. ✅ Same SDK and API key as the rest of LangSmith ✅ Any framework or custom code ✅ N

Almost everyone is building agent harness systems the wrong way. The default move: pick LangChain or LangGraph or the OpenAI Agents SDK, accept the loop, the tools, the memory, the orchestration, the

As agent harnesses become more standardized, we’re going to see a lot more “managed agent services” Managed Deep Agents lets you create a managed Deep Agent without standing up a custom agent server.

Harrison Chase shared a positive experience attending Boston Tech Week and highlighted interaction with Blitzy AI. The post captures participation in a technology industry event in Boston and engageme

Evals shape agent behavior. Every eval is a vector that shifts the behavior of your agentic system. More evals ≠ better agents. Instead, build targeted evals that reflect desired behaviors in producti

Governance should lead to more building, not less Agent development is so much more fun when people don't need to ration tokens because one runaway agent might blow up the bill Understand spend, cap t

join us for a behind the curtains look at LangSmith Engine (our agent that helps make your agent better) One of the best ways to learn what LangSmith Engine is capable of is to talk to the team that b

Managed Deep Agents lets you create a managed Deep Agent without standing up a custom agent server. Our runtime supports: ✅ Durable threads ✅ Streaming runs ✅ Checkpointing ✅ Human-in-the-loop workflo

Deep Agents v0.6 introduces ContextHubBackend, a versioned storage system for agent behavior files integrated with LangSmith Context Hub. This feature enables agents to access and manage the files and

One of the best ways to learn what LangSmith Engine is capable of is to talk to the team that built it. Join @bentannyhill for a live session on June 11th and see how your team can automate the agent