AI app for it and development · no coding needed
Isolated agent sandbox delivery workspace
Run agent code in isolated cloud sandboxes and return the files and artifacts it produces.
Made for: Engineering teams and AI product owners running agent code that must produce files and artifacts
What it does for you
The problem
Agent code runs on local machines or ad-hoc infrastructure, so runs are hard to isolate, resume, monitor and turn into retrievable deliverables.
What it gives you
Reviewed, retrievable deliverables produced by the agent run
What you give it
Agent messagesrepositoriesattached filespackage needsmodel keys
Build your own version of Agent Sandbox, Epho and more
One app with what these 4 AI tools do, yours to keep and change: Agent Sandbox, Epho, The Cloud for AI Agents, Autoblocks 2.0.
Everything these tools do, in one app
- Isolated sandbox execution Runs agent code in a sandboxed environment separated from the user's local machine.Found in Agent Sandbox, Epho, The Cloud for AI Agents
- Cloud sandbox provisioning Spins up cloud sandboxes for agent workloads without the user managing the infrastructure.Found in Agent Sandbox, Epho, The Cloud for AI Agents
- Persistent sandbox state Keeps the sandbox's full state across runs so agents can continue where they left off.Found in Agent Sandbox, The Cloud for AI Agents
- Python and Bash execution Lets agents run Python and Bash code inside the sandbox.Found in Agent Sandbox
- Package installation Allows agents to install packages within a session.Found in Agent Sandbox
- File and artifact storage Stores agent files and artifacts in the cloud so uploaded data can be reused.Found in Agent Sandbox
- Artifact generation and retrieval Lets agents produce and return deliverables such as charts, PDFs, and datasets.Found in Agent Sandbox
- Compact API surface Provides a small API that reduces setup work to get agents running.Found in Agent Sandbox, Epho, The Cloud for AI Agents
- Single-request agent run Starts an agent run by posting a message and streaming back the work.Found in Epho
- Pre-configured coding agents Runs Claude Code, Codex, or Opencode already set up inside the sandbox.Found in Epho
- Repo and file context Connects repositories and attaches files so the agent starts with context.Found in Epho
- Provider fallback Automatically falls back across sandbox providers to avoid session failures.Found in Epho
- Bring your own API keys Uses the user's own Anthropic, OpenAI, or Opencode keys for model access.Found in Epho
- Fast micro-VM startup Boots Firecracker micro-VM sandboxes in roughly 100-150 ms.Found in The Cloud for AI Agents
- Full Linux access Gives each sandbox full Linux access for a wide range of workloads.Found in The Cloud for AI Agents
- Language SDKs and templates Offers official JavaScript and Python SDKs, beta SDKs for other languages, and ready-to-use templates.Found in The Cloud for AI Agents
- No runtime limits Avoids cold-start delays and enforced runtime limits so sandboxes can run longer.Found in The Cloud for AI Agents
- Drag-and-drop workflow builder Builds automation workflows by dragging and dropping task blocks.Found in Autoblocks 2.0
- App integrations Connects with multiple popular apps and services for automation.Found in Autoblocks 2.0
- Monitoring and notifications Provides real-time monitoring and notifications on task progress.Found in Autoblocks 2.0
- Custom triggers and conditions Lets users fine-tune automation with custom triggers and conditions.Found in Autoblocks 2.0
- Analytics dashboard Tracks automation performance in a comprehensive dashboard.Found in Autoblocks 2.0
How it works, step by step
- Provision isolated cloud sandboxes per run
- Keep full sandbox state across runs
- Execute Python and Bash inside the sandbox
- Install packages within a session
- Store agent files and artifacts in the cloud
- Generate and retrieve charts, PDFs and datasets
- Expose a compact API for setup
- Start a run by posting one message and streaming the work
- Run pre-configured coding agents inside the sandbox
- Attach repositories and files as starting context
- Fall back across sandbox providers on failure
- Use the buyer's own model API keys
- Boot micro-VM sandboxes quickly with full Linux access
- Offer language SDKs and ready templates
- Build workflows by dragging and dropping task blocks
- Connect apps, monitor progress, set triggers and conditions, and track performance in a dashboard
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed, retrievable deliverables set 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 agent sandbox 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 agent sandbox 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 Cloudflare25 KB
- prompt-vps.mdThe same build on your own server (Docker)25 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria11 KB
- demo/index.htmlThe working demo on sample data200 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
Run agent code in isolated cloud sandboxes and return the files and artifacts it produces. For engineering teams and AI product owners running agent code that must produce files and artifacts, convert agent messages, repositories, attached files, package needs and model keys into reviewed, retrievable deliverables. The benefit is a testable hypothesis, measured through accepted artifact sets per run and reruns caused by sandbox or environment failures; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect agent messages, repositories, attached files, package needs and model keys, then follow this sequence: 1. Provision isolated cloud sandboxes per run. 2. Keep full sandbox state across runs. 3. Execute Python and Bash inside the sandbox. 4. Install packages within a session. 5. Store agent files and artifacts in the cloud. 6. Generate and retrieve charts, PDFs and datasets. 7. Expose a compact API for setup. 8. Start a run by posting one message and streaming the work. 9. Run pre-configured coding agents inside the sandbox. 10. Attach repositories and files as starting context. 11. Fall back across sandbox providers on failure. 12. Use the buyer's own model API keys. 13. Boot micro-VM sandboxes quickly with full Linux access. 14. Offer language SDKs and ready templates. 15. Build workflows by dragging and dropping task blocks. 16. Connect apps, monitor progress, set triggers and conditions, and track performance in a dashboard. Resolve uncertain cases with qualified reviewers, approve reviewed, retrievable deliverables, and measure accepted artifact sets per run and reruns caused by sandbox or environment failures against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Sandbox isolation, provider fallback and key handling remain infrastructure concerns; final artifact acceptance and release decisions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, key handling, sandbox isolation and usage permissions. Named owners approve artifact release and external actions. One sandbox provider and one pre-configured coding agent; final artifact acceptance and release decisions remain human. 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 sandbox provider and one pre-configured coding agent; final artifact acceptance and release decisions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: provision isolated cloud sandboxes per run; keep full sandbox state across runs. Support the remaining modules with operator review: execute Python and Bash inside the sandbox; install packages within a session; store agent files and artifacts in the cloud; generate and retrieve charts, PDFs and datasets. 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
Buyer-owned repositories, file stores and model provider keys. Cloud asset storage, design-file import/export and publishing destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Run setup and context, Live sandbox run, Artifact review and delivery. Use a run list for projects, a large central run view with streamed output, and a right-hand panel for sandbox state, packages, keys and comments. Let users compare runs and artifact versions side by side. Display queued, running, needs review and delivered states. Provide a client preview link with comments anchored to the relevant artifact. Make the task-specific outcome reviewed, retrievable deliverables visible beside its evidence, review state and value baseline.
