AI agents have dominated Twitter/X the past few days: Claude Code, Remotion, Cursor, etc. But most developers are running these locally. Terminal agents on your machine.
That's useful, but it's a different game when you're building AI apps for users. The moment your agent needs to actually execute something e.g., run a Python script, process a PDF, generate files, you're suddenly building sandboxing infrastructure, managing VMs, handling file storage.
It's a massive distraction from your actual product. We built Bluebag to solve this for Vercel AI SDK users. Two lines of code, and your agent gets access to Skills that execute in managed sandboxes.
No Docker orchestration. No K8s. Dependencies, file handling, signed download URLs, all handled.
The post walks through some of the architecture (progressive skill loading, auto-provisioned VMs, multi-tenant isolation) and shows concrete integration examples.
Happy to answer questions about the approach or trade-offs we made.
ohans•1h ago
AI agents have dominated Twitter/X the past few days: Claude Code, Remotion, Cursor, etc. But most developers are running these locally. Terminal agents on your machine.
That's useful, but it's a different game when you're building AI apps for users. The moment your agent needs to actually execute something e.g., run a Python script, process a PDF, generate files, you're suddenly building sandboxing infrastructure, managing VMs, handling file storage.
It's a massive distraction from your actual product. We built Bluebag to solve this for Vercel AI SDK users. Two lines of code, and your agent gets access to Skills that execute in managed sandboxes.
No Docker orchestration. No K8s. Dependencies, file handling, signed download URLs, all handled.
The post walks through some of the architecture (progressive skill loading, auto-provisioned VMs, multi-tenant isolation) and shows concrete integration examples.
Happy to answer questions about the approach or trade-offs we made.
Cheers