Join us at Google Cloud Next for a panel on the future of developer experience. Karan Singh, Partnerships Lead at LangChain, will join leaders from Atlassian, Datadog, Harness, and Google Cloud. Together, they’ll explore how AI agents and open standards are eliminating friction
@langchain
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DeepAgents introduces filesystem permissions for secure resource management
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🔒 new in deepagents: filesystem permissions shared resources and org-wide policies are exactly the kind of files you want your agent to read but never overwrite. filesystem permissions let you enforce that with simple declarative allow/deny rules!
→ View original post on X — @langchain, 2026-04-13 19:16 UTC
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Your Harness Equals Your Memory in LangChain
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your harness = your memory if you'd rather read in blog form, link here: blog.langchain.com/your-harn… Harrison Chase (@hwchase17) x.com/i/article/204292509094… — https://nitter.net/hwchase17/status/2042978500567609738#m
→ View original post on X — @langchain, 2026-04-11 14:53 UTC
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Flint: Autonomous AI Platform Generates On-Brand Landing Pages
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LangSmith for Startups Spotlight: @tryflint
— LangChain (@LangChain) 10 avril 2026
Flint is an autonomous website platform that generates on brand landing pages. Flint’s marketing agent works behind the scenes to get your message out to the world.
Flint is trusted by companies like Cognition, Graphite, 11x, and… pic.twitter.com/qDk8kY8IPsLangSmith for Startups Spotlight: @tryflint Flint is an autonomous website platform that generates on brand landing pages. Flint’s marketing agent works behind the scenes to get your message out to the world. Flint is trusted by companies like Cognition, Graphite, 11x, and
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Building Better Agents: Traces, Evaluation, and Continuous Improvement
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What does it actually take to make agents better over time? A system that starts with a trace. You capture traces of agent behavior, enrich them with evaluations and human feedback, identify what’s failing and why, make targeted changes, and validate those changes before
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Enterprise AI Agents at Scale: Interrupt 2026 Conference Preview
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Interrupt 2026 is back May 13–14 in San Francisco.
This year's question: how do you make agents work at enterprise scale? Here's a preview of what's on the agenda:
→ Apple on building a low-code agent platform for 15K+ employees
→ Lyft on building evals that actually tell -
Scaling AI Agents Safely with LangChain at Google Cloud
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Join Victor Moreira, Deployed Engineer at LangChain, at Google Cloud Next. He’ll be joined by Jeremy Olmsted-Thompson, Principal Engineer at Google Cloud, and Brandon Royal, Product Manager at Google Cloud, to cover how to scale AI agents while safely running untrusted,
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Incorporating Human Judgment in Agent Improvement Loop
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New Guide: Incorporating human judgment in the agent improvement loop
— LangChain (@LangChain) 10 avril 2026
Building agents is hard. Everyone talks about the code. What gets less attention is how to capture domain expert knowledge and actually get it into your agent.
The most successful teams follow an agent… pic.twitter.com/LDIQr5h0AfNew Guide: Incorporating human judgment in the agent improvement loop Building agents is hard. Everyone talks about the code. What gets less attention is how to capture domain expert knowledge and actually get it into your agent. The most successful teams follow an agent
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Claude Agent SDK Tracing LangSmith Upgrade Features
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Claude Agent SDK tracing in LangSmith just got an upgrade. Now you can trace:
→ Subagents
→ Child runs inside MCP tools
→ Cost tracking + more Update to the latest Python SDK to try it out. Docs: https://
docs.langchain.com/langsmith/trac
e-claude-agent-sdk
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Max Agency Podcast: Building Production AI Agents with Hex
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🎙️Introducing Max Agency
— Harrison Chase (@hwchase17) 9 avril 2026
Max Agency is a new podcast where we go deep on how the best agents are actually being built: architecture decisions, tradeoffs, evals, and everything in between. Each episode, I sit down with engineering leaders who are doing this work in production.… pic.twitter.com/yqBsBcGQOR🎙️Introducing Max Agency Max Agency is a new podcast where we go deep on how the best agents are actually being built: architecture decisions, tradeoffs, evals, and everything in between. Each episode, I sit down with engineering leaders who are doing this work in production. Our first episode features Izzy Miller (@isidoremiller), AI Engineer at Hex (@_hex_tech). Hex has been shipping data agents since before most teams were even thinking about them, starting with single-cell text-to-SQL and graduating to a full Notebook agent that can work autonomously for 20 minutes on a complex analysis. Izzy has a lot of perspective on what it actually takes to get agents working well in production, and what breaks along the way. A few takeaways from our conversation: – Keep your eval sets small enough to hold in your head: Izzy runs 30-50 handcrafted "traps" with multiple repetitions, rather than hundreds of variants. If you can't explain why your agent fails each one, your eval set is too big – Day zero performance is almost irrelevant: The more interesting question is how the agent compounds. Izzy is building a 90-day simulation where the warehouse evolves and the agent has to accumulate understanding – You can catch agent errors without seeing the raw outputs: By running an LLM-as-a-judge over production usage and clustering the results, you can surface places where something likely went wrong, without needing to read individual conversations Watch the full episode on: – Youtube: piped.video/watch?v=Xyh1Eqcj… – Apple Podcasts: podcasts.apple.com/us/podcas… – Spotify: open.spotify.com/episode/1BJ…
→ View original post on X — @langchain, 2026-04-09 16:32 UTC