🏗️ Why Are So Many Companies Building Their Own AI Agent Sandboxes?

If you let an AI agent write code, analyze data, or manage systems on your behalf — would you give it unrestricted access to everything?

That's the question sparking heated debate on Hacker News, where hundreds of developers are discussing why companies keep building custom sandboxes instead of using off-the-shelf solutions.

The consensus points to four key reasons:

**Security must be bespoke.** Generic tools don't understand which data is sensitive in your specific environment. Every company has unique attack surfaces.

**Long-term cost control.** Cloud-based sandbox services charge per execution. When your AI agent runs hundreds of commands daily, costs spiral fast.

**Granular permissions.** Some teams need AI to access only specific folders, APIs, or services — a level of control most generic platforms can't offer.

**Latency matters.** Round-tripping to external APIs for every sandbox operation is too slow for production workflows.

Think of it like onboarding a brilliant new employee you don't fully trust yet. You define which rooms they can enter, which tools they can use, and what damage they can't do.

A sandbox is that controlled workspace — letting AI operate at full capacity without breaking anything outside.

The bigger signal: as AI agents evolve from tools-you-command into assistants-that-think, the infrastructure to contain them is becoming just as important as the models themselves.