Own your intelligence. Makers of LangSmith, @LangChain_OSS, and @LangChain_JS.
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Next stop on the LangSmith Roadshow? Chicago!
Hear about the current state of agent development, and get ready for two hands-on workshops focused on Deep Agents and LangSmith Engine.
Interested in attending? RSVP today!
events.langchain.com/LangSmi…
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We have several LangChain-led and community-led meetups coming up! RSVP and bring a friend!
🇩🇪 10/27 Munich
🇺🇸 10/29 Washington, DC
🇲🇽 10/29 Mexico City
🇦🇪 10/31 Dubai
🇨🇦 11/4 Toronto
🇺🇸 11/5 San Francisco
🇬🇧 11/12 London
🇺🇸 11/12 Boston
🇺🇸 11/18 Seattle
🇸🇪 11/25 Stockholm
🇩🇪 12/4 Hamburg
RSVP: luma.com/langchain
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Join us, @ExtendHQ, and @tryramp this Thursday for a private screening of You Can See Everything in San Francisco.
Seats are limited, save yours now! luma.com/27b59c8i
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LangChain retweeted
Content Studio is a truly epic example of a company harness built on Managed Deep Agents. It is multiplayer by nature, I work with it in a public channel so others can weigh in on the design process
our design agent at @LangChain has shipped 3,000+ assets since august, and posts with its work pulled ~4.5M views on socials (it made this promo too)
it’s become the design brain for the whole org. decks, diagrams, ad creatives, launch films and more, all made from slack
we encoded our designers’ judgment into its skills, a design wiki and brand checks that run on every render, so it knows what good looks like for us
then we built a custom media harness around it: a headless photoshop, a shared visual canvas (thanks @paper), video tools like @HyperFrames_ plus our own animation engine, our brand assets and a library of design references
it runs on Managed Deep Agents, which abstracts away a ton of infra including sandboxes, agent memory, slack integration, scheduling and more.
we are going through the architecture, use cases and impact live at our oct 28 webinar 👇
events.langchain.com/gtm-eng…
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On October 28th, go behind the scenes of our design agent.
We’ll demo the agent live, walk through how we built it, and share what we’ve learned from using an agent for creative production across the company.
💻 RSVP: events.langchain.com/gtm-eng…
our design agent at @LangChain has shipped 3,000+ assets since august, and posts with its work pulled ~4.5M views on socials (it made this promo too)
it’s become the design brain for the whole org. decks, diagrams, ad creatives, launch films and more, all made from slack
we encoded our designers’ judgment into its skills, a design wiki and brand checks that run on every render, so it knows what good looks like for us
then we built a custom media harness around it: a headless photoshop, a shared visual canvas (thanks @paper), video tools like @HyperFrames_ plus our own animation engine, our brand assets and a library of design references
it runs on Managed Deep Agents, which abstracts away a ton of infra including sandboxes, agent memory, slack integration, scheduling and more.
we are going through the architecture, use cases and impact live at our oct 28 webinar 👇
events.langchain.com/gtm-eng…
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Want to learn more about agent memory?
We're hosting a virtual workshop on November 3rd! events.langchain.com/registe…
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LangChain retweeted
amazing to see such a successful example of using agent traces in LangSmith to fine tune a model! Was awesome working with the @corridor team on this
.@corridor used smithtune’s trace-based workflow to build the training data for Argos, a fine-tuned 9B model that scores the highest of the 14 models they tested on their security context benchmark.
🧵 Full thread ⤵️
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LangChain retweeted
Hats off to @Aditi and the team at @corridor for the release of Argos 1! 🎩
This is an awesome example of agent trajectories in LangSmith -> post-trained model in practice, with 𝚜𝚖𝚒𝚝𝚑𝚝𝚞𝚗𝚎 helping make it possible.
They sft'd qwen3.5-9B and ended up with a model that beats gpt-6, opus 5.5, and gemini 3.6 flash on both quality and response time when selecting relevant security guidance for coding agents.
This kind of process is a great way to build alpha into your agent, and we're super bullish on giving teams the ability to use their own data to drive improvements at the model layer.
@farzaankai and I are so excited to share what we've been working on over the last few months.
Introducing Argos: Corridor's first security model.
Special thanks to @baseten and @LangChain for all your support.
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.@corridor used smithtune’s trace-based workflow to build the training data for Argos, a fine-tuned 9B model that scores the highest of the 14 models they tested on their security context benchmark.
🧵 Full thread ⤵️
Introducing corridor-argos-1, Corridor’s first security model.
A Qwen 3.5 9B fine-tune that outperforms frontier models including GPT-6 Astra and Claude Opus 5.5 on security context selection, with the lowest time to first token of the 14 models we tested.
Thread 🧵
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Context Engineering explained in 5 minutes by @jess_weng18.
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LangChain retweeted
Agent evals raise a lot of questions. We rounded up the most common ones and answered them! Take a look 👀
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Agents are being asked to take on increasingly complex work, from research and coding to workflows that span multiple tools and steps.
The challenge is giving them the structure to do that work reliably.
Join our APAC + ANZ Agent Harnesses & Deep Agents 101 Virtual Workshop to explore the building blocks behind agents designed for longer-horizon tasks and learn how to apply those patterns in practice.
events.langchain.com/registe…
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LangChain retweeted
great points by caspar on what makes building a "company harness" (companyOS? org harness?) hard
and how MDA solves them!
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: langch.in/mda
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LangChain retweeted
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: langch.in/mda
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More Managed Deep Agents resources:
🎓 LangChain Academy Module: academy.langchain.com/course…
💻 Managed Deep Agents 101: events.langchain.com/on-dema…
⏯️ YouTube Tutorial playlist: piped.video/watch?v=xdrB53bg…
What is a Managed Deep Agent?
How does it differ from a Deep Agent?
How do you get started?
@jess_weng18 from the LangChain Academy team answers all of these questions in 2 minutes
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LangChain retweeted
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
langchain.com/blog/langsmith…
What is a Managed Deep Agent?
How does it differ from a Deep Agent?
How do you get started?
@jess_weng18 from the LangChain Academy team answers all of these questions in 2 minutes
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What is a Managed Deep Agent?
How does it differ from a Deep Agent?
How do you get started?
@jess_weng18 from the LangChain Academy team answers all of these questions in 2 minutes
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Human In The Loop: Now in beta in Managed Deep Agents
approve / reject isn't a question
sometimes your agent needs to actually ask you something. so let it: drop a few buttons in the slack thread ⏸️ keep going when you click ▶️
public beta in @LangChain Managed Deep Agents
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📍 Toronto
📅 Nov 3
We’re teaming up with @TryArcade for a half-day, hands-on workshop.
We'll build a loan-processing agent and take it from first run to governed, observable deployment.
The patterns carry over to any agent that touches customer data, moves money or changes records.
Interested in attending? RSVP below. arcade.dev/events/toronto-ro…
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