Managed Deep Agents: Managed, model-agnostic infra for deep agents you can deploy with a single line of code.
@langchain
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Build no-code agents with everyday language using LangSmith Fleet
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With LangSmith Fleet, you can start creating agents using every day language.
— LangChain (@LangChain) 4 juin 2026
Enroll in our LangChain Academy Quickstart course to learn how to build no-code agents for real work. https://t.co/k2AhiIz7fB pic.twitter.com/LLYyyT1oIpWith LangSmith Fleet, you can start creating agents using every day language. Enroll in our LangChain Academy Quickstart course to learn how to build no-code agents for real work. https://
academy.langchain.com/courses/quicks
tart-agent-builder
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Max Agency: Building AI Agents for Scientific Work with Benchling’s Head of AI
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On the latest episode of Max Agency, @hwchase17
— LangChain (@LangChain) 4 juin 2026
sat down with @nlarusstone, Head of AI at @benchling for a conversation on building agents for scientific work.
⏯️ YouTube: https://t.co/kd6rxBTugR
🎧 Apple Podcasts: https://t.co/IK5uLBNDAC
🎧 Spotify: https://t.co/sG80P8CnyL pic.twitter.com/siurE40tREOn the latest episode of Max Agency, @hwchase17 sat down with @nlarusstone
, Head of AI at @benchling for a conversation on building agents for scientific work. YouTube: https://
youtube.com/watch?v=RjpTrf
fSMjE
… Apple Podcasts: https://
podcasts.apple.com/us/podcast/the
-tool-design-tricks-behind-benchlings-ai-agents/id1891551672?i=1000771169985
… Spotify: https://
open.spotify.com/episode/2bFEj2
W290bk2JW1zC6wyp
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LangSmith Engine automates agent team improvement loop from traces
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Most agent team runs the same manual improvement loop:
— LangChain (@LangChain) 4 juin 2026
Trace → find failure patterns → fix prompts or code → create evals → test → ship → repeat
LangSmith Engine helps turn production traces into named issues, root-cause analysis, proposed fixes, and stronger eval… pic.twitter.com/TozrvKwfTHMost agent team runs the same manual improvement loop: Trace → find failure patterns → fix prompts or code → create evals → test → ship → repeat LangSmith Engine helps turn production traces into named issues, root-cause analysis, proposed fixes, and stronger eval
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LLM Gateway in LangSmith: traceable governance and live updates
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Most governance tools live in their own consoles or dashboards. LLM Gateway lives in LangSmith. When requests are blocked or info is redacted, you get traceable events. See what your agents do, update system prompts or tool configs, re-evaluate against existing test sets, all
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Building a bespoke harness for agent context: guide by Sydney Runkle
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Agents are only as good as the context you give them. The job of a harness is to get the model the right context at the right time for a given task. Here’s a guide from @sydneyrunkle on how to build a bespoke harness for your use case.
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LangChain Supports NVIDIA Nemotron 3 Ultra with Deep Agents
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LangChain supports @NVIDIA Nemotron 3 Ultra out of the box, with Day 0 support for Deep Agents. As a member of the Nemotron Coalition, we are excited to work with NVIDIA to make it even easier and more accessible to share and build on top of open models.
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Full study on efficient verifiers for legal agents
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Read our full study: https://
langchain.com/blog/designing
-efficient-verifiers-for-legal-agents
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LangChain Labs study with Harvey on verifier efficiency benchmarking
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In our LangChain Labs study with @Harvey
, we looked at how to measure efficiency across verifier designs. We benchmarked 5 setups against Sonnet per-criterion as the reference. -
LangSmith Engine reviews traces, learns from usage, updates Context Hub
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You can use LangSmith Engine to review your agent traces to find bugs and areas for improvement across agent prompts + code. Between runs, the agent can review conversations, learn from real usage, and update Context Hub files.