LangSmith Engine: The agent's agent. pic.twitter.com/0oBp3rM2AX
— LangChain (@LangChain) 16 juin 2026
LangSmith Engine: The Agent of Agents
@langchain
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LangSmith Engine: The Agent of Agents
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Agent: successful response but task failure
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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
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Traces and Evals: From Debugging to Continuous Improvement Loop
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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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Fleet turns a chat into an agent for your daily applications
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Describe a task in a short prompt. Fleet devises a plan, takes action, and works with the applications your team uses daily. Turn a chat into an agent in just a few clicks.
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LangSmith Sandboxes: agent access to systems with credential and network policy control
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LangSmith Sandboxes gives agents access to the systems they need, while keeping credentials and network policy under platform control.
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Max Agency podcast on building the best agents
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Listen to the full conversation on Max Agency, a podcast about how the best agents are being built. 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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The biggest obstacles in building scientific agents according to nlarusstone
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In the latest Max Agency, @hwchase17 asked @benchling Head of AI @nlarusstone about the biggest blockers in building agents for scientific work. pic.twitter.com/XKS6Nnj5mv
— LangChain (@LangChain) 15 juin 2026In the latest Max Agency, @hwchase17 asked @benchling's Head of AI @nlarusstone what were the biggest obstacles in building agents for scientific work.
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LangChain uses LLM gateway to control coding agent expenses
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A developer using coding agents can accumulate thousands of dollars in weekly expenses before anyone notices. It happened at LangChain, so we built a solution. How we use the LLM gateway internally
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Deep Agents: Execution Environment, the Backbone
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Deep Agents deep dive from @SydneyRunkle | Part 1
— LangChain (@LangChain) 15 juin 2026
The execution environment, or the backbone of a Deep Agent. pic.twitter.com/baoiUDGvxuDeep Agents: Deep Dive by @SydneyRunkle | Part 1 The execution environment, or the backbone of a Deep Agent.
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Lyft builds 8 AI agents solving 35% of customer problems
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.@Lyft built 8 AI agents that are capable of fully resolving 35% of all customer issues.
— LangChain (@LangChain) 15 juin 2026
At Interrupt, they shared the evals used internally, how they scale evals with LangSmith, and the lessons learned along the way. https://t.co/SHmYnOo84p pic.twitter.com/VDz7VsdTGN@Lyft built 8 AI agents capable of completely solving 35% of all customer problems. At Interrupt, they shared the evaluations used internally, how they scale evaluations with LangSmith, and the lessons learned throughout