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At Interrupt, @AndrewYNg sat down with @hwchase17 for a fireside chat on the future of agents.
— LangChain (@LangChain) 17 juin 2026
Watch the session in full: https://t.co/26MwWWqjZc pic.twitter.com/zAt8vHlpJY
At Interrupt, @AndrewYNg sat down with @hwchase17 for a fireside chat on the future of agents. Watch the full session: https://youtu.be/OaRhpwz_TGM
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The next phase of agent development in financial services will be measured by trust, control, and production readiness. In our latest guide, we look at how @jpmorgan, @Chime, and Bridgewater approach agents of
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LangSmith lets your agents unpack.
— LangChain (@LangChain) 16 juin 2026
Understand what didn’t work out in production, identify what matters, and make continuous improvements. pic.twitter.com/CYUnq88jgb
LangSmith allows your agents to fully express themselves. Understand what went wrong in production, identify what really matters, and make continuous improvements.
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Agent governance is not something that should be added to agentic systems. LangSmith LLM Gateway allows you to apply rules on the same platforms where agents are built, observed, and evaluated.
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You run multi-step workflows where state must persist across tool calls. You need bursting capability (i.e., thousands of parallel environments for RL training or evaluations) that must go from zero to scale in
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Deep Agents deep dive Part 2 | Context Management
— LangChain (@LangChain) 16 juin 2026
A <2 min explanation on one of the most important capabilities in the Deep Agents harness from @SydneyRunkle pic.twitter.com/tnIsx9aiLi
Deep Agents deep dive Part 2 | Context Management A
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LangSmith Fleet offers two types of agents: General purpose chat / Specialized agents @BraceSproul on why we offer a choice, and when to use each.
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LangSmith Engine: The agent's agent. pic.twitter.com/0oBp3rM2AX
— LangChain (@LangChain) 16 juin 2026
LangSmith Engine: The Agent of Agents
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Traces show the inputs, model calls, tool calls, outputs, and final action. Evals turn those learnings into a way to test whether the next version is better. This is how teams move from manual debugging to a continuous improvement loop. Join @hwchase17 for a deep dive on June
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An agent can return a "successful" response and still fail at the task. It may call the wrong tool, omit an approval step, use the wrong context, or produce a response that seems correct but is not. That is why teams