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Up next at Interrupt:
— LangChain (@LangChain) 13 octobre 2026
🎙️ @GregorBeuster on building visual agents at DB Engineer & Consulting with Deep Agents
🎙️@VictorMoreira16 on what it takes to deploy mission-critical agents to production pic.twitter.com/KBmJXZUPqM
Up next at Interrupt: 🎙️ @GregorBeuster on building visual agents at DB Engineer & Consulting with Deep Agents 🎙️
@VictorMoreira16 on what it takes to deploy mission-critical agents to production
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The 3 questions every agent builder should be able to answer:
— LangChain (@LangChain) 9 octobre 2026
✅ Where the agent fails
✅ How to reproduce it
✅ How to make it stop
Watch @JakeBroekhuizen’s session: https://t.co/yQkzaV0GVt pic.twitter.com/YFfv9Dtf52
The 3 questions every agent builder should be able to answer: ✅ Where the agent fails
✅ How to reproduce it
✅ How to make it stop Watch @JakeBroekhuizen
’s session: https://
youtu.be/oVKGKdIAgtg
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New in Managed Deep Agents 0.9: Reactions API for @SlackHQ Channels.
— LangChain (@LangChain) 9 octobre 2026
When it comes to building effective user agent experiences, loading states are underrated.
Now, you can build loading states and receipts using either heuristics, decision models, or custom logic. https://t.co/DTIKwOSmdq pic.twitter.com/94x0p5zDlZ
New in Managed Deep Agents 0.9: Reactions API for @SlackHQ Channels. When it comes to building effective user agent experiences, loading states are underrated. Now, you can build loading states and receipts using either heuristics, decision models, or custom logic.
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We simplified agent authentication, memory, and channels, and brought in web search as a pre-built tool for Managed Deep Agents.
— LangChain (@LangChain) 9 octobre 2026
Watch @VictorMoreira16’s full Interrupt NYC session for an inside look: https://t.co/W2CQTpwhdv pic.twitter.com/fn4D8JzH6A
We simplified agent authentication, memory, and channels, and brought in web search as a pre-built tool for Managed Deep Agents. Watch @VictorMoreira16
’s full Interrupt NYC session for an inside look: https://
youtu.be/XcsspzECTKA
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LangSmith Signals from the last month: ✅ Claude Sonnet 5: #9 to #2 in model adoption. 51% more orgs using it ✅ gpt 5.6 luna: #3 to #1 in call footprints. 65% more calls ✅ Two open-weights models are now in the adoption top 10, but not for call volume ✅ Smaller, faster
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✅ 60k queries handled
✅ 500+ customer accounts
✅ 85%+ of sessions resolved without a support ticket
✅ 250+ cases auto-detected + escalated to the right team @SnykSec Assist. Built on LangChain + LangGraph. Observability in LangSmith. Full story: https://
langchain.com/blog/how-snyk-
turned-an-internal-support-agent-into-a-customer-feature
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Here’s an app we built called Restock, an office supply agent that works in @SlackHQ.
— LangChain (@LangChain) 8 octobre 2026
A quick demo ⏯️ https://t.co/3HTMdFSkcL pic.twitter.com/Ugrepjp6sw
Here’s an app we built called Restock, an office supply agent that works in @SlackHQ
. A quick demo ⏯️
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You can now build an agent that:
🔎 Finds real products
🛒 Prepares purchases
💳 Pays
…with a person approving every order 👨💻 Ask in @slackhq
, review the order, approve in @Stripe
's Link agent wallet. Built on MPP + Managed Deep Agents.
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Teams are narrowing in on skills as the standard for providing domain knowledge to agents. Here's how we've revamped skills in Deep Agents to meet growing demand!
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Managed Deep Agents v0.9 is here! ✅ Schedules SDK: Agents create their own reminders, follow-ups, and recurring tasks mid-conversation ✅ Per-run config: Pick the model, skills, MCP servers, and sandbox for each run, so one deployment serves every team or repo ✅ Slack