Cloud infrastructure was built for humans, not AI agents. Here’s why that’s a problem.

Traditional cloud hosting was designed for a human-first workflow: a developer writes code locally, commits to Git, triggers a multi-minute CI/CD build, and deploys to production once or twice a day.

As software development shifts from human-only coding to AI-assisted and autonomous agent loops, that infrastructure model starts breaking down.

The New Infrastructure Problem:

When an AI agent (like Claude, Cursor, or a custom LLM loop) generates code and wants to test, preview, or deploy it:

1. Security is a nightmare: Executing LLM-generated code directly on your local machine or primary backend opens you up to prompt injections, unintended file changes, and server exploits.

2. Dependency & build friction: Package installations (npm install, pip install), multi-file builds, and runtime setup take time and clutter your local or production environment.

3. DevOps overhead: An AI shouldn't have to manage complex Dockerfiles, YAML manifests, or manual cloud setups just to run a test script or spin up a preview.

What we're building:

I’m building Adios.dev to give AI agents and engineering teams isolated, automated environments to write, test, and deploy code safely.

• Model Context Protocol (MCP) Integration: Connect your AI client directly to scoped tools so the agent can inspect project files, run builds, open preview environments, and read runtime logs inside a controlled boundary.

• Isolated Previews & Execution: Let AI agents install dependencies, run build commands, and generate live previews without touching your primary infrastructure or breaking production.

• Human-in-the-Loop Safeguards: Let AI test and iterate freely in disposable sandboxes, but keep live production releases behind explicit human approval gates.

I'm currently building this in public as a solo founder, actively tackling the hard engineering problems around optimizing build times, package caching, and preview environments for AI workflows.

Check out Adios.dev and let me know—how are you handling code execution and previews for AI agents in your stack today?