Built an AI Agent That Wrecked My Prod Database. Here's How I Fixed That

Let me paint you a picture.

It's 2 AM. Your AI coding agent is "just fixing a small bug." You wake up to 47 Slack notifications, a corrupted database, and a $340 bill from your cloud provider for whatever chaos it spun up overnight.

Sound familiar? No? Just me? Cool, cool.

Here's the thing nobody talks about when they hype up AI agents: isolation is the unsolved problem. Everyone's focused on making agents smarter. Nobody's focused on making them safe to actually run.

The Dirty Secret of AI Agent Infrastructure

When you let an AI agent execute code, browse the web, run terminal commands, or interact with APIs, you're handing it keys to your kingdom.

Most devs handle this one of three ways:

1. Run it locally and pray it doesn't nuke something important ☠️
2. Use your existing cloud and get surprise bills + security nightmares
3. Pay for sandboxing services and bleed money at scale

Option 3 sounds great until you look at the pricing. E2B, the most popular sandbox provider, charges in ways that absolutely do not scale when you're running agents in production. I've seen teams spend more on sandboxing than on their entire compute budget.

What We Actually Need

I've been building AI agent pipelines for a while now, and what the ecosystem is missing is embarrassingly simple:

Secure, isolated environments that spin up fast and don't cost a fortune.

That's it. That's the whole ask.

A VM that:

• Is completely isolated (your agent can go feral and it doesn't matter)
• Spins up in minutes, not hours
• Doesn't require a DevOps PhD to configure
• Has sane, predictable pricing

I kept building this infrastructure from scratch for every project. So eventually, we just... productized it.

Introducing Coasty

Coasty gives your AI agents their own secure cloud VMs — purpose-built for agentic workloads.

The key details:

• Full VM isolation — each agent session gets its own environment
• 2-5 minute setup — ready fast enough for real workflows
• 70% cheaper than E2B — we've seen teams cut infra costs dramatically
• Optimized for AI agents — not just generic cloud VMs duct-taped together
• OSWorld benchmark: 82% — we actually test agent performance, not just uptime

Why This Matters More Than You Think

The agent space is moving fast. But most teams are still duct-taping together their own sandboxing with Docker containers, pray-it-works firewall rules, and a lot of technical debt.

I spent an embarrassing amount of time on a recent project just solving "how do I let this agent run code without destroying everything?" It's not a fun problem. It's not where your engineering hours should go.

The future of AI agents isn't just smarter models, it's trustworthy infrastructure that lets you actually deploy them in production without a prayer circle.

Who This Is For

• AI/ML engineers building production agent pipelines
• Developer tool companies giving their users code execution
• Startups integrating agents into their product without a dedicated DevOps team
• Researchers running automated experiments at scale

The Real Talk

We're early. We have paying customers, 300+ developers signed up, and we're actively iterating based on real usage. This isn't vaporware, but we're also not pretending we've solved everything.

What we have solved is the thing that kept biting me: running AI agents safely without an infrastructure team or a VC-sized cloud budget.

Try It

We're onboarding developers and anyone else now.

→ coasty.ai, join the waitlist or reach out directly

If you're building anything with AI agents and hitting the "how do I run this safely" wall, I'd genuinely love to talk. Drop a comment or find me on Twitter/X.

What's your current setup for running AI agents in production? Curious how others are handling isolation, drop it in the comments.**