Managed persistent agent deployment workspace

For

Engineering teams and platform operators running AI agents as production services

Solves

Agents built in temporary sandboxes lose state, cannot be retried or resumed, and lack the deployment, routing, observability and billing controls needed to run as persistent services.

Delivers

A deployed, versioned agent service with API access, logs and rollback

Built in

about 6 weeks of creation time, MVP in 7 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

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

Run agents as managed, persistent services instead of one-off sandbox executions.

1. Deploy an agent from a command, prompt or API call.
2. Keep agent sessions alive across restarts and long gaps.
3. Run agents continuously beyond temporary sandbox executions.
4. Expose agents via REST and streaming APIs.
5. Route tasks to suitable models with provider fallbacks.
6. Provide built-in tools for web search, file operations and API calls.
7. Connect agents to external systems and SaaS tools.
8. Monitor behavior with logs, execution history and dashboards.
9. Validate behavior with evaluation suites and generated tests.
10. Manage immutable versions with one-click rollbacks.
11. Store API keys and secrets in an encrypted vault.
12. Isolate each agent in a container or sandbox.
13. Charge users per message with a revenue split.
14. Collaborate in dedicated team workspaces.
15. Customize agent appearance to hide infrastructure.
16. Replace default templates with custom Docker images.
17. Access agents through a native mobile app.
18. Self-host the platform on your own infrastructure.

Everything these tools do, in one app

• Agent deployment Deploy an AI agent quickly using a simple command, prompt, or API call.Found in OpenComputer, Agent 37 Cloud, Kodosumi and 1 more
• Persistent agent sessions Agent state is kept alive across restarts and long gaps, enabling retry and resume.Found in OpenComputer, Fulcrum Agent Rentals, Linchpin
• Always-on execution Agents run continuously and remain active beyond temporary sandbox executions.Found in OpenComputer, Agent 37 Cloud
• API access Expose agents via REST or streaming APIs for integration with other services.Found in OpenComputer, Kopai, Kodosumi and 1 more
• Model routing Automatically route tasks to suitable models, with options for multiple providers and fallbacks.Found in Logic, Fulcrum Agent Rentals, Linchpin
• Built-in tools Provide agents with pre-built tools like web search, file operations, and API calls.Found in Logic, Linchpin
• External integrations Connect agents to external systems and SaaS tools through pre-wired integrations.Found in Logic, Agent 37 Cloud, Kopai
• Observability and logging Monitor agent behavior with logs, execution history, and analytics dashboards.Found in Logic, Agent 37 Cloud, Kopai
• Evaluation and testing Validate agent behavior with built-in evaluation suites, generated tests, and certification.Found in Logic, Kopai
• Versioning and rollbacks Manage agent versions with immutable versioning and one-click rollbacks.Found in Logic
• Credential vault Securely store API keys and secrets locally with encryption.Found in Linchpin
• Sandbox isolation Run each agent in an isolated container or sandbox for security.Found in Agent 37 Cloud, Linchpin
• Monetization Charge users per message and manage billing with a revenue split.Found in Kopai
• Team workspaces Collaborate with team members in dedicated workspaces.Found in Kopai
• Branding controls Customize the agent's appearance to hide underlying infrastructure.Found in Agent 37 Cloud
• Custom Docker images Replace default templates with custom Docker images for proprietary tooling.Found in Agent 37 Cloud
• Mobile app access Access agents via a native mobile app for on-the-go use.Found in Fulcrum Agent Rentals
• Open-source and self-hostable Run the platform on your own infrastructure with an open-source license.Found in Kodosumi, Linchpin

What goes in, what comes out

The workflow

1. In
   Start with

   Agent code, prompts, credentials, routing rules and integration settings

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect agent code

4. 3

   Prompts

5. 4

   Credentials

6. 5

   Routing rules and integration settings

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   A deployed, versioned agent service with API access, logs and rollback

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 fixed deployment target and approved model set; final production release 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: Agent registry and deployment, Live session and execution monitor, API and integration settings. Use a list of deployed agents with status and version, a central execution timeline with logs and retry controls, and a right-hand panel for routing, tools, credentials and integrations. Let users compare versions and roll back. Display running, paused, failed and rolled-back states. Provide a client-facing API key page with usage and billing. Make the task-specific outcome a deployed, versioned agent service with API access, logs and rollback visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, agent versions, team workspaces, API keys, usage allowances, billing records, rollback 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

Customer-owned agent code, authorized model providers and permitted SaaS tools. Cloud container runtime, secret stores, logging destinations and billing providers. Start with file exchange and validate destination specifications before promising direct deployment. 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: deploy an agent from a command, prompt or API call; keep agent sessions alive across restarts and long gaps. 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 platform operators running AI agents as production services use it to solve "agents built in temporary sandboxes lose state, cannot be retried or resumed, and lack the deployment, routing, observability and billing controls needed to run as persistent services"?
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: Successful agent invocations per operator hour and recovery time after restart.
4. Measure, then decide. Track successful agent invocations per operator hour and recovery time after restart; 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 fixed deployment target and approved model set; final production release and security checks remain engineering. Implement one approved input format, a bounded representative case set and the first two task modules: deploy an agent from a command, prompt or API call; keep agent sessions alive across restarts and long gaps. Support the third module with operator review: run agents continuously beyond temporary sandbox executions. 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 a deployed, versioned agent service with API access, logs and rollback. Retain the explicit scope boundary: One fixed deployment target and approved model set; final production release and security checks remain engineering.

What the build depends on. Agent upload and preview, asynchronous deployment 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 fixed deployment target and approved model set; final production release 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 agent service. Offer a monthly production allowance after repeat demand. Quote complex integrations or custom Docker work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded deployed, versioned agent service with API access, logs and rollback. 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 agents as managed, persistent services instead of one-off sandbox executions. Demonstrate a concrete deployed, versioned agent service with API access, logs and rollback using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

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

Lead magnet

A reviewed sample deployed, versioned agent service with API access, logs and rollback from a small authorized input set, with a transparent calculation of successful agent invocations per operator hour and recovery time after restart and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams and platform operators running AI agents as production services and inspect a recent example of agents built in temporary sandboxes lose state, cannot be retried or resumed, and lack the deployment, routing, observability and billing controls needed to run as persistent services.
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 successful agent invocations per operator hour and recovery time after restart, 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: Successful agent invocations per operator hour and recovery time after restart. 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

Successful agent invocations per operator hour and recovery time after restart; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a deployed, versioned agent service with API access, logs and rollback. 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 deployment patterns, routing rules 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 platform operators running AI agents as production services. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

OpenComputer, Logic, Lamatic.ai, Lamatic 2.0, Agent 37 Cloud, Kopai, Fulcrum Agent Rentals, Kodosumi, Linchpin and Octoverse. Compare this product with the buyer's present method on successful agent invocations per operator hour and recovery time after restart. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, container runtime, 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 a deployed, versioned agent service with API access, logs and rollback. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve agent behavior, source attribution, credential security and usage permissions. Engineering owners approve substantive changes and production scope. One fixed deployment target and approved model set; final production release 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.