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✅ Take your agent from first run to production-ready through iterative cycles of improvement.
— LangChain (@LangChain) 18 juin 2026
✅ Learn how to observe and evaluate agents with LangSmith
✅ Build reliable agents that last pic.twitter.com/pXS87Pk47x
Take your agent from first run to production-ready with iterative improvement cycles. Learn to observe and evaluate agents with LangSmith. Build reliable agents that last.
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Building a useful agent is becoming easier. Running them in production remains difficult. We built Managed Deep Agents so your team can focus on agent behavior rather than rebuilding the execution environment around it.
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LangSmith sandboxes are now a first-class environment in @harborframework Installation:
pip install "harbor[langsmith]" Authentication:
export LANGSMITH_API_KEY="" That's it. Your evaluations are now ready to run
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We partnered with @FireworksAI_HQ to answer the following question… How can we economically exploit important signals from each unique trace while maintaining state-of-the-art performance? Read our LangChain Labs study
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Fine-tuning open models can surpass or match state-of-the-art models. Base
@Alibaba_Qwen
out of the box with good prompting:
Solid for perceived error classification, below state-of-the-art model performance. With a LoRA SFT job:
Both
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Deep Agents deep dive part 3 | Delegation
— LangChain (@LangChain) 17 juin 2026
TLDR:
A planning tool that allows the model to organize work for challenging tasks.
Equipped with subagent support out of the box.
A 90 second explanation from @SydneyRunkle pic.twitter.com/6GTyaGjpeg
Deep Agents deep dive part 3 | Delegation TLDR:
A planning tool that allows the model to organize work for complex tasks. Equipped with built-in support for sub-agents from the start. A 90-second explanation by
@SydneyRunkle
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Introducing On-Call Copilot – a new agent template in LangSmith Fleet
— LangChain (@LangChain) 17 juin 2026
→ Triages every alert and investigates root cause across your code and traces
→ Manages tickets and routes every incident to the right channels
→ Continuously learns from your runbooks, escalation rules,… pic.twitter.com/5SM2FmJE7L
Introduction of On-Call Copilot – a new agent model in LangSmith Fleet
→ Sorts each alert and investigates root cause across your code and traces
→ Manages tickets and redirects each incident to the right channels
→ Continuously learns from your
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✅ RemoteGraph for deployment ↔ deployment interaction
— LangChain (@LangChain) 17 juin 2026
✅A2A Protocol support out of the box
✅ Interact with your agent via MCP Protocol@VictorMoreira16 on Agent Interoperability in Managed Deep Agentshttps://t.co/wiQVO5luru pic.twitter.com/z8Gwco88Ar
RemoteGraph for interaction deployment deployment A2A protocol support out of the box Interact with your agent via the MCP protocol @VictorMoreira16 on agent interoperability in managed deep agents https://
langchain.com/blog/introduci
ng-managed-deep-agents
…
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Agent improvement often starts with a manual review process: digging into production traces, identifying recurring failure patterns, comparing executions, and deciding what to fix first. On June 24, discover how LangSmith Engine helps
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With LangSmith LLM Gateway, detection, investigation, and remediation take place on the same surface where agents are built.
Fewer tools
Fewer context switches
Policy events that arrive alongside the trace data that accompany them