Isolated agent sandbox delivery workspace

For

Engineering teams and AI product owners running agent code that must produce files and artifacts

Solves

Agent code runs on local machines or ad-hoc infrastructure, so runs are hard to isolate, resume, monitor and turn into retrievable deliverables.

Delivers

Reviewed, retrievable deliverables produced by the agent run

Built in

about 6 weeks of creation time, MVP in 7 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

Run agent code in isolated cloud sandboxes and return the files and artifacts it produces.

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.
17. Compare the reviewed result with the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before consequential use.
19. Export a versioned reviewed, retrievable deliverables set with source references and unresolved questions.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Agent messages, repositories, attached files, package needs and model keys

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect agent messages

4. 3

   Repositories

5. 4

   Attached files

6. 5

   Package needs and model keys

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Reviewed, retrievable deliverables produced by the agent run

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

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

Integrations and data access

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.

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

   7 days

   One buyer segment, one recurring use case; first modules: provision isolated cloud sandboxes per run; keep full sandbox state across runs. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 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 teams and AI product owners running agent code that must produce files and artifacts use it to solve "agent code runs on local machines or ad-hoc infrastructure, so runs are hard to isolate, resume, monitor and turn into retrievable deliverables"?
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 artifact sets per run and reruns caused by sandbox or environment failures.
4. Measure, then decide. Track accepted artifact sets per run and reruns caused by sandbox or environment failures; 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 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.

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, retrievable deliverables. Retain the explicit scope boundary: One sandbox provider and one pre-configured coding agent; final artifact acceptance and release decisions remain human.

What the build depends on. Sandbox provisioning, streaming run output, 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 sandbox provider and one pre-configured coding agent; final artifact acceptance and release decisions remain human.

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 run package. Offer a monthly production allowance after repeat demand. Quote complex multi-provider or high-volume workloads separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, retrievable deliverables set. 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

Run agent code in isolated cloud sandboxes and return the files and artifacts it produces. Demonstrate a concrete reviewed, retrievable deliverables set using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams and AI product owners running agent code that must produce files and artifacts professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, retrievable deliverables set from a small authorized input set, with a transparent calculation of accepted artifact sets per run and reruns caused by sandbox or environment failures and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams and AI product owners running agent code that must produce files and artifacts and inspect a recent example of agent code runs on local machines or ad-hoc infrastructure, so runs are hard to isolate, resume, monitor and turn into retrievable deliverables.
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 artifact sets per run and reruns caused by sandbox or environment failures, 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 artifact sets per run and reruns caused by sandbox or environment failures. 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 artifact sets per run and reruns caused by sandbox or environment failures; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, retrievable deliverables. 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 run configurations, sandbox 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 teams and AI product owners running agent code that must produce files and artifacts. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Agent Sandbox, Epho, The Cloud for AI Agents and Autoblocks 2.0, plus local scripts and self-managed cloud infrastructure. Compare this product with the buyer's present method on accepted artifact sets per run and reruns caused by sandbox or environment failures. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Sandbox compute, storage, provider fallback attempts, 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, retrievable deliverables. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.