Isolated AI coding agent delivery workspace

For

Engineering leads and platform teams running AI coding agents on production repositories

Solves

AI coding agents run on shared machines, install packages and open pull requests without isolation, reviewable evidence or a repeatable delivery path.

Delivers

Reviewed agent pull requests with sandbox logs and test evidence

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce unsafe agent actions and review effort while keeping the team's own workflow.

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.
5. Handle tasks asynchronously.
6. Stream output for real-time feedback.
7. Support multiple agent and model providers.
8. Keep the codebase open for inspection and extension.
9. Provide a TypeScript implementation for JavaScript and TypeScript projects.
10. Emit telemetry for monitoring and visibility.
11. Deploy the platform with one click.
12. Offer phase-wise debugging during generation.
13. Provide a chat-based development flow.
14. Export projects to external repositories.
15. Add observability and caching for operations.
16. Give each agent its own cloud machine and filesystem.
17. Snapshot a template box and clone it for new threads.
18. Provide desktop, CLI and mobile clients.
19. Scan local setup and port dependencies and environment to the cloud.
20. Schedule automated tasks.
21. Integrate with Slack for notifications and interactions.
22. Run headless browsers and test suites such as Playwright.
23. Compare the reviewed result with the recorded baseline and value assumptions.
24. Capture corrections and named-owner approval before merge.
25. Export a versioned reviewed agent pull requests with sandbox logs and test evidence with source references and unresolved questions.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Authorized repositories, agent instructions, dependency manifests and test suites

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect authorized repositories

4. 3

   Agent instructions

5. 4

   Dependency manifests and test suites

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed agent pull requests with sandbox logs and test evidence

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, repository versions, reviewer comments, approval states, sandbox allowances, run limits, export history and a rights record for supplied code. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: run AI-generated code in isolated sandboxed environments; generate code from agent instructions. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will engineering leads and platform teams running AI coding agents on production repositories use it to solve "AI coding agents run on shared machines, install packages and open pull requests without isolation, reviewable evidence or a repeatable delivery path"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted agent pull requests per engineering hour and reverted agent commits.
4. Measure, then decide. Track accepted agent pull requests per engineering hour and reverted agent commits; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed agent pull requests with sandbox logs and test evidence. Retain the explicit scope boundary: One authorized repository and one agent provider per pilot; final merge and security checks remain engineering.

What the build depends on. Repository upload and preview, asynchronous agent jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist engineering QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One authorized repository and one agent provider per pilot; final merge and security checks remain engineering.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined repository package. Offer a monthly production allowance after repeat demand. Quote complex multi-repository or regulated environments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed agent pull requests with sandbox logs and test evidence. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce unsafe agent actions and review effort while keeping the team's own workflow. Demonstrate a concrete reviewed agent pull requests with sandbox logs and test evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering leads and platform teams running AI coding agents on production repositories professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed agent pull requests with sandbox logs and test evidence from a small authorized input set, with a transparent calculation of accepted agent pull requests per engineering hour and reverted agent commits and no promised savings.

The first 30 days

1. Week 1: interview five engineering leads and platform teams running AI coding agents on production repositories and inspect a recent example of AI coding agents run on shared machines, install packages and open pull requests without isolation, reviewable evidence or a repeatable delivery path.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted agent pull requests per engineering hour and reverted agent commits, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted agent pull requests per engineering hour and reverted agent commits. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted agent pull requests per engineering hour and reverted agent commits; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed agent pull requests with sandbox logs and test evidence. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved agent configurations, repository constraints and review examples, together with reliable delivery for a narrow engineering niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering leads and platform teams running AI coding agents on production repositories. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

VibeKit, VibeSDK by CloudFlare and Boxes.dev, plus self-managed agent scripts and CI pipelines. Compare this product with the buyer's present method on accepted agent pull requests per engineering hour and reverted agent commits. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Sandbox compute, model calls, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed agent pull requests with sandbox logs and test evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.