Agents Virtual Machine is live, the first purpose-built environment tailored for agents. Your AI agents can now think, plan, and execute. Get started at https://t.co/kwoYVfsgJZ

Focusing on the full lifecycle devs need more than just tools to build agents; they need infrastructure to deploy, monitor and scale them. $AVM closes the gap between prototype and production, providing the full lifecycle support agents demand!

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If agents can think and execute code autonomously, they all need a secure execution environment... every single one of them. $AVM is the infrastructure layer for all AI generated code. It empowers developers to deploy AI generated workloads to production without touching DevOps

With a bit of delay, sharing a photo dump from Devconnect in Buenos Aires, 17–22 November. AVM was there ⚡️

📌V2 Development Update BACKEND ✅ Free plan sandbox limits on resources and total instances are now enforced ✅ File upload/download fully supported FRONTEND ✅ Improved validation during sandbox creation ✅Environment variables now work similar to Vercel (Pro mode + key:value

Whether you're a nurse, physician, APP or pharmacist, ASTCT membership connects you to the education, clinical tools and professional community you need to grow in your HCT/cell therapy career.

The Testing Paradox: How do you test an agent that makes non deterministic decisions? You need infrastructure that supports deterministic replays, A/B testing, and rollback capabilities when agents behave unexpectedly. That's AVM 🗳️

You've built your agent. Now you need to deploy. But only now you start to see versioning conflicts and environment mismatches between dev and prod. This is a problem agents can inherit from traditional software environments. To be effective, agents need to reliably (and

Agents don't just need data access, they need to execute arbitrary code on it, too. Without proper isolation, you're one malicious line of code away from a breach. Sandbox it!

AI agents are now clearly moving from “pilot” to production. The underlying problem? Most "agent adoption" is just bolting AI onto existing workflows and not rethinking HOW things get done under the hood. The companies that avoid tomorrow's bottlenecks today are rebuilding from

Function1 delivered. Key takeaways from our team: 1. We saw strong investor interest in AI infrastructure throughout the event and most conversations were about real production challenges teams are hitting. The market for execution infrastructure is early, and it's growing fast.

fnctn1's wrapped up! It was amazing being shoulder to shoulder with giants and showing them what AVM is capable of directly. Now it's back to coding.⚡️

Function1

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fnctn1 kicks off tomorrow, all systems go! We'll be on the first panel of the day alongside @crunchbase, @seedgroupme, @alumniventures, and @Techstars discussing AI startups in the midst of BigTech👇 https://t.co/shlceOKv2l

Running one agent is simple. Orchestrating dozens in perfect harmony, that’s the real-world application we're solving.🔍 When Agent A hands off to Agent B, who manages state? How do you prevent bottlenecks when Agent C depends on both? What happens when Agent D fails mid-task?

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Docker fixed the dev’s biggest headache. AI agents need the same solution. An agent that runs perfectly in YOUR environment can fail in production because of: - Different Python versions - Missing system dependencies - Conflicting library installations - Environment variable

5/ This is what agent orchestration looks like 👇 🤖↩️⤵️ 🤖↔️🤖⏩🌐⤵️ 🤖⤴️🤖⏩💾⏩🤖⏩📊🏁 🤖⤵️↗️ 💾🤖↩️ They're not deploying once and running forever. They're spinning up environments hundreds of times per day: analyze data, generate charts, execute code, tear down. Repeat.

4/ Enter the Sandbox. 📦 - Hardware isolations similar to VMs - Faster deployment than containers - Minimal resource overhead .. and this is exactly what agents need. Agile, ephemeral zones to get things done safely, at scale, then fade away.

3/ Containers 🐋 - Process-level isolation - Shares the host's kernel - Quicker startup (1-10 seconds) - Much lower resource overhead But that shared kernal? While your agent just wrote some quick code to do an easy task, it unknowingly discovered an exploit that just

AI agents are moving from demos to teammates. Part I of our playbook covers the foundations: what an agent is, how it’s structured (objective, instructions, tools, content grounding), and how to spot high-impact use cases—so you can ship secure, reliable agents at scale.

2/ Good ol' Virtual Machines 💻 - Maximum hardware isolation - Slow startup time - Heavy resource overhead All this is great for a typical enterprise workloads. Emails, high throughput and data intensive applications. But for an AI agent's grunt work? Way too slow to deploy,

1/ We didn't need lightning-fast, secure code execution environments until AI agents arrived. Now we do. Agents generate unpredictable code in real-time. They need to execute it instantly, safely, AND at scale. Here's why VMs and containers both fall short, and why sandboxes