In this meet-up, we'll cover how to: Deploy agents with durable execution so runs survive crashes, deploys, and long waits for human input Use checkpoints and memory stores Add human-in-the-loop gates for consequential decisions We'll also discuss how teams
AGENTS
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Technical Requirements for Deploying Long-Running AI Agents
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What does it actually take to deploy long-running agents and the runtime capabilities that make it possible? Join us for an evening in New York with
– Robert Xu, Deployed Engineering @LangChain – Austin Berke, Lead AI Product Engineer @harmonic_ai RSVP: Robert Xu, Deployed -
Anthropics co-founder predicts autonomous self-improving AI by 2028
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Anthropics co-founder Jack Clark:
— Chubby♨️ (@kimmonismus) 8 mai 2026
"My prediction is that by the end of 2028, it is more likely than not that we will have an AI system where you could say to it:
"Make a better version of yourself."
And it would simply go off and do that completely autonomously."
Its coming. pic.twitter.com/OEM7zwcQzcAnthropics co-founder Jack Clark: "My prediction is that by the end of 2028, it is more likely than not that we will have an AI system where you could say to it: "Make a better version of yourself." And it would simply go off and do that completely autonomously." Its coming.
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Mirage unifies backends into one virtual filesystem for AI agents
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AI agents just got one virtual filesystem across every backend. Mirage mounts S3, Drive, Slack, Gmail, GitHub, Linear, Notion, and Postgres under a single root. Agents read and write across them using the same bash commands every model already knows. The team rewrote bash
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Try Gemma 4 Assistant GGUF Builds on Hugging Face
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You also want to give these Gemma 4 assistant GGUF builds a try on @huggingface →
https://
huggingface.co/collections/At
omicChat/gemma-4-assistant-gguf
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Pourquoi l’approche modulaire fonctionne en pratique
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Why the building block approach actually scales in the real world: → You can compose agents instead of rebuilding them
→ You control behavior by design, not by prompt hacking
→ You can change one block without breaking the whole system This is how you go from experiments to -
AI as a System of Small Functions, Not a Giant Brain
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The biggest “aha” from this conversation with Arno at Elastic: AI should not be one giant brain. → It should be a system of small, well-defined functions
→ Each function has a clear role
→ Each agent only gets the power it needs, nothing more This is how you avoid chaos as -
AI initiatives fail due to agent power imbalance
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Most AI initiatives don’t fail because the models are weak.
— Ronald van Loon (@Ronald_vanLoon) 8 mai 2026
They fail because teams give agents too much power, or not enough.
That’s the real scaling problem nobody talks about, and it’s exactly what I discussed with Arno van de Velde, Principal Solutions Architect at… pic.twitter.com/4AJ6GwNAIMMost AI initiatives don’t fail because the models are weak. They fail because teams give agents too much power, or not enough. That’s the real scaling problem nobody talks about, and it’s exactly what I discussed with Arno van de Velde, Principal Solutions Architect at
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Checkpointing feature improves AI agent workflows
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this is the most underrated update in the agent space right now.
— Akshay 🚀 (@akshay_pachaar) 8 mai 2026
your AI workflow runs for 47 minutes, burns 312 LLM calls, then crashes at step 8.
most frameworks make you restart from zero.@crewAIInc just shipped checkpointing. think google docs autosave, but for your… pic.twitter.com/wEJffLBlVLthis is the most underrated update in the agent space right now. your AI workflow runs for 47 minutes, burns 312 LLM calls, then crashes at step 8. most frameworks make you restart from zero. @crewAIInc just shipped checkpointing. think google docs autosave, but for your