earlier this ~spring, models got good enough to drive browsers in some ways, browsers are the new terminal! a general purpose tool one difference is there is a few different ways to design tools to drive the browser. @sydneyrunkle deep dives into how we did this for MDA

A lot of content engineering challenges have pretty standard solutions, which has lead to some engineers saying agent engineering is solved/boring True to some extent, but there are a bunch of (fun) new ones emerging as agents evolve!

deep agents is the harness - the agent loop managed deep agents adds what you need once lots of people call the same agent: each user's own credentials and their own memory

SO MUCH THIS I believe that one of jev's greatest benefits to automation will come from resurrecting architecture best practices: state management, encapsulation, abstraction !!!! and combining them with ML's best practices: measure/evaluate, use calibration/uncertainty (adding screenshot b/c I don't know how to quote 2 posts 🤦)

LangGraph maps extremely well onto Jev. Not because it was built for decision models specifically, but because its core abstraction is already about putting decisions into state, then routing based on them. I wonder if over time we’ll think about LangGraph less as agent orchestration and more as just the way you build software with composable models.

today: come hang and work/tinker at Builders and Brews NYC with @tavilyai ! i'll be here all day with @LangChain engineers and our Education team and PMs! tinkering + coffee + matcha. 👉

jev and langgraph are a great combo! modeling agents as complex systems, with AI imbued in them, makes a ton of sense langgraph is the best way to model them as such, and using Jev inside langgraph to turbo charge all the small decisions that need to be made is awesome combo

Ok, this is insanely cool @langchain is launching a model-tuning service w/@FireworksAI_HQ & @baseten You can fine-tune your own custom model with the LangSmith Traces you already have - then compare & deploy! 👀 Hello SmithTune 🫡

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

Trajectories make it easier than ever to analyze long agent runs. They are now a first-class citizen within LangSmith. We also spent a lot of time ensuring our trajectory format handles things like deferred tool calling and context compaction well.