Managed Deep Agents can now be steered mid-turn. If you send a message while the agent is working, it reads it at its next model call instead of waiting for the turn to finish. Small change that makes agents much, much nicer to work with

Company harnesses break down knowledge silos Three in four requests to Content Studio come from people outside marketing. This usage pattern is why I'm so bullish on company harnesses: they allow everyone to get involved in domains they had no business in before. We've encoded our design team's judgment into a set of skills anyone can use, so anyone with a creative vision can execute it with Content Studio Coding agents brought the same shift to SWE. People who would have called themselves nontechnical a year ago are now shipping PRs I'm excited to see this happen in every domain

How do you build a company harness? We've built internal agents at LangChain across marketing, sales, and engineering. The results have been incredible but these agents were NOT easy to get right. We spent a ton of time solving the problems that come with a multiplayer harness. Every agent team landed on the same core primitives: - It lives where people already work (for us that's Slack) - It knows who's asking and acts OBO that person for some services and with its own identity for others - It has memory, scoped globally and/or at the user level - It runs code in a sandbox where authenticated CLIs and scripts just work (gh, curl, your internal APIs) - Large % of its runs are scheduled rather than explicitly invoked by someone (ambient agents!!) It became clear we needed one shared place for these primitives, so we built Managed Deep Agents. In my humble opinion it is the easiest way to build a company harness today Our docs are very agent-friendly, point them here to get started:

Managed Deep Agents can read the files people share and send files back where the conversation already is. It feels like magic Shoutout to @SlackHQ, their files API made this a joy to build

we have seen many companies launch managed agent products recently. today, we released 𝚖𝚊𝚗𝚊𝚐𝚎𝚍-𝚍𝚎𝚎𝚙𝚊𝚐𝚎𝚗𝚝𝚜 0.8 I have been asked a few times why managed agent frameworks are useful in an era where "code is free". here is my take: generating code is easy, this is a fact. but as a developer, you still need to decide how your system should behave and apply those decisions consistently across your codebase: whose credentials does the agent use? what information can it share? what changes when another person joins the conversation? your auth, memory and channel integrations need to follow the same rules, including as you add features. this is hard to scale without a solid, opinionated foundation I do not think every team needs to solve these problems from first principles. there is value in building on decisions that other people have thought through, implemented and tested MDA is our managed service built on our open source Deep Agents harness. we build those decisions into managed primitives with sensible, configurable defaults, and handle the supporting infrastructure that is why I think Managed Deep Agents should be the default starting point for new agents. start with a foundation that we've thought through, and take on more of the system as your requirements demand it if you hit a limit with MDA, dm me. I want to understand what is missing and see if we can fix it. either way, MDA is built on our open source Deep Agents framework, so if you still need more control, you can move off MDA and use Deep Agents directly with your own integrations and infra re: diagram ~ this is one of those decisions we made: user memory is available in slack dms, but not in shared channels by default. fully configurable btw

𝚖𝚊𝚗𝚊𝚐𝚎𝚍-𝚍𝚎𝚎𝚙𝚊𝚐𝚎𝚗𝚝𝚜 0.8 is here and I think it is the best thing since sliced bread things we shipped: 🧠 agent and user memory with access policies 🌐 connect webhooks with the new HTTP channel ✋ improved human-in-the-loop support 💬 Slack channels now support images and files 🔑 improved OAuth connection options 🔒 authenticate sandbox requests via a secure proxy 🔎 search the web with Parallel (thanks @p0) 📁 a runtime sandbox files API released for js and py!

good overview on the managed agents landscape by @Sam_Witteveen we've seen many major ai companies ship a managed agent product over the past few months. this space will only continue to grow exponentially langchain first built a managed agent product two years ago - langsmith deployment - a self-hostable runtime for agent workloads. LSD has evolved with our open source ecosystem and is used today for hosting anything from simple langgraph workflows to complex apps built on deepagents harness this space has come a long way in two years. managed deployment was the first step. the next is to give devs a complete foundation for their agents, so they don't have to assemble it themselves. that's why we built managed deep agents: you bring the the agent's intelligence (its instructions, skills, tools, model) and we provide a managed runtime (LSD agent server, session management, credentials, tracing) out of all the options listed below, MDA is the only one built on an open source harness. if you're building agents, see how much time you could save:

this is really cool hadn’t thought enough about agent auth this way sometimes the agent should act as itself. sometimes it should act as me making that a choice in the runtime makes a ton of sense

holy sh*t. this is f**king insane. langchain just documented the missing memory layer for agents that are supposed to improve after every run. instead of stuffing everything into one giant prompt, deep agents can keep long-term memory, reusable skills and lessons from previous tasks. [it takes a few minutes to understand the loop] 1/ run the task 2/ save what actually worked 3/ load that lesson into the next run that’s how an agent stops starting from zero every single time.

Agent auth is hard, but it shouldn't be! Your agent needs to act as someone: sometimes that's one shared identity, sometimes it's the person asking. To get either right you'll need an OAuth app for each service, a token store, refresh logic, and a consent flow Today we're fixing this with Managed Connections: define who your agent acts as in code, and the rest is handled for you: 𝚊𝚐𝚎𝚗𝚝 -> agent uses its own token 𝚞𝚜𝚎𝚛 -> agent uses the user's token, guiding them through the consent flow if they haven't connected yet With a single argument you avoid the whole OAuth dance. Available now, in 𝚖𝚊𝚗𝚊𝚐𝚎𝚍-𝚍𝚎𝚎𝚙𝚊𝚐𝚎𝚗𝚝𝚜 0.7

mcp is alive and accelerating: - mcp's tier 1 sdks are nearing 500m dls per month - mcp went stateless, making servers much easier to scale and redeploy - HITL is now native to the protocol - tool discovery can now be cached for faster agent startup now in 𝚕𝚊𝚗𝚐𝚌𝚑𝚊𝚒𝚗

managed deep agents feels like magic. it handles the entire slack setup for you, and if you haven't picked an icon, it derives one from the agent's name. ~2M possible icons! inspired by dither-kit's DitherAvatar, thanks @grimcodes.