AI app for it and development · no coding needed
Isolated AI coding agent delivery workspace
Reduce unsafe agent actions and review effort while keeping the team's own workflow.
Made for: Engineering leads and platform teams running AI coding agents on production repositories
What it does for you
The problem
AI coding agents run on shared machines, install packages and open pull requests without isolation, reviewable evidence or a repeatable delivery path.
What it gives you
Reviewed agent pull requests with sandbox logs and test evidence
What you give it
Authorized repositoriesagent instructionsdependency manifeststest suites
Build your own version of VibeKit, VibeSDK by CloudFlare and more
One app with what these 3 AI tools do, yours to keep and change: VibeKit, VibeSDK by CloudFlare, Boxes.dev.
Everything these tools do, in one app
- Secure sandboxed execution Runs AI-generated code in isolated environments to prevent unsafe operations.Found in VibeKit, VibeSDK by CloudFlare, Boxes.dev
- AI code generation Enables AI agents to write code automatically.Found in VibeKit, VibeSDK by CloudFlare
- Package installation Allows AI agents to install packages as part of their tasks.Found in VibeKit
- Pull request creation Lets AI agents open pull requests automatically.Found in VibeKit
- Asynchronous task handling Supports running tasks asynchronously for efficient execution.Found in VibeKit
- Streaming feedback Provides streaming output for real-time feedback during task execution.Found in VibeKit
- Model-agnostic support Works with multiple AI coding agents or LLM providers, allowing flexibility.Found in VibeKit, VibeSDK by CloudFlare
- Open-source codebase Source code is available for inspection and extension.Found in VibeKit, VibeSDK by CloudFlare
- TypeScript implementation Written in TypeScript for easy integration into JavaScript/TypeScript projects.Found in VibeKit
- Telemetry Provides built-in telemetry for monitoring and visibility.Found in VibeKit
- One-click deployment Deploys the platform to CloudFlare with a single click.Found in VibeSDK by CloudFlare
- Phase-wise debugging Offers debugging assistance in phases during code generation.Found in VibeSDK by CloudFlare
- Chat-based development flow Provides an interactive chat interface for development.Found in VibeSDK by CloudFlare
- Project export Exports projects to external repositories or accounts.Found in VibeSDK by CloudFlare
- Observability and caching Includes observability and caching features to simplify operations.Found in VibeSDK by CloudFlare
- Per-agent cloud machines Gives each AI agent its own cloud VM and filesystem to avoid conflicts.Found in Boxes.dev
- Template with snapshots Creates a main cloud box, snapshots it, and clones for new threads to preserve context.Found in Boxes.dev
- Multi-platform clients Provides desktop, CLI, and mobile apps for interacting with threads.Found in Boxes.dev
- Local setup porting Automatically scans local dev setup and ports dependencies and environment to the cloud.Found in Boxes.dev
- Scheduled automations Allows scheduling automated tasks.Found in Boxes.dev
- Slack integration Integrates with Slack for notifications or interactions.Found in Boxes.dev
- Headless browser and test support Supports running headless browsers and test suites like Playwright.Found in Boxes.dev
How it works, step by step
- Run AI-generated code in isolated sandboxed environments
- Generate code from agent instructions
- Install packages inside the sandbox
- Open pull requests automatically
- Handle tasks asynchronously
- Stream output for real-time feedback
- Support multiple agent and model providers
- Keep the codebase open for inspection and extension
- Provide a TypeScript implementation for JavaScript and TypeScript projects
- Emit telemetry for monitoring and visibility
- Deploy the platform with one click
- Offer phase-wise debugging during generation
- Provide a chat-based development flow
- Export projects to external repositories
- Add observability and caching for operations
- Give each agent its own cloud machine and filesystem
- Snapshot a template box and clone it for new threads
- Provide desktop, CLI and mobile clients
- Scan local setup and port dependencies and environment to the cloud
- Schedule automated tasks
- Integrate with Slack for notifications and interactions
- Run headless browsers and test suites such as Playwright
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before merge
- Export a versioned reviewed agent pull requests with sandbox logs and test evidence with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Isolated AI coding agent delivery workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Isolated AI coding agent delivery workspace with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare24 KB
- prompt-vps.mdThe same build on your own server (Docker)24 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria11 KB
- demo/index.htmlThe working demo on sample data203 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce unsafe agent actions and review effort while keeping the team's own workflow. For engineering leads and platform teams running AI coding agents on production repositories, convert authorized repositories, agent instructions, dependency manifests and test suites into reviewed agent pull requests with sandbox logs and test evidence. The benefit is a testable hypothesis, measured through accepted agent pull requests per engineering hour and reverted agent commits; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect authorized repositories, agent instructions, dependency manifests and test suites, then follow this sequence: 1. Run AI-generated code in isolated sandboxed environments. 2. Generate code from agent instructions. 3. Install packages inside the sandbox. 4. Open pull requests automatically. Resolve uncertain cases with qualified reviewers, approve reviewed agent pull requests with sandbox logs and test evidence, and measure accepted agent pull requests per engineering hour and reverted agent commits against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One authorized repository and one agent provider per pilot; final merge and security checks remain engineering. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve code ownership, source attribution, license accuracy and usage permissions. Engineering owners approve substantive changes and merge scope. One authorized repository and one agent provider per pilot; final merge and security checks remain engineering. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One authorized repository and one agent provider per pilot; final merge and security checks remain engineering. Implement one approved input format, a bounded representative case set and the first two task modules: run AI-generated code in isolated sandboxed environments; generate code from agent instructions. Support the third module with operator review: install packages inside the sandbox. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Customer-owned repositories, authorized agent providers and permitted test suites. Cloud sandbox infrastructure, repository hosting, CI systems and notification destinations. Start with file exchange and validate destination specifications before promising direct merge. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Repository and agent setup, Live task run, Review and merge. Use a task list for runs, a central run view with streaming output, and a right-hand panel for sandbox state, diffs, logs and approvals. Let users compare agent branches side by side. Display queued, running, needs review and merged states. Provide a reviewer link with comments anchored to the relevant diff. Make the task-specific outcome reviewed agent pull requests with sandbox logs and test evidence visible beside its evidence, review state and value baseline.
