Managed agent hosting and operations console

For

Small technical teams and solo builders running AI agents for their own workflows

Solves

Running AI agents means setting up servers, wiring integrations, storing keys and repairing broken instances by hand.

Delivers

A source-linked assistant and administrator console

Built in

about 5 weeks of creation time, MVP in 5 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

Reduce infrastructure and maintenance work while keeping agent data and keys under the owner's control.

1. Provision a ready-to-run OpenClaw environment.
2. Start a working agent in under a minute.
3. Connect Gmail, Slack, Notion and similar services through pre-configured OAuth.
4. Accept the owner's own API keys and model accounts.
5. Run agents and store data on the owner's machine.
6. Retain useful context across sessions.
7. Create several specialized agents with separate configurations.
8. Reach the agent from desktop, terminal and chat surfaces.
9. Provide browser shell access for scripts and background tasks.
10. Show live activity with in-browser chat intervention.
11. Attempt automatic repair of a broken instance.
12. Back up instance data daily.
13. Build agents and workflows through a visual no-code interface.
14. Route each task to a suitable model.
15. Share assistants, skills and histories with role-based access.
16. Store files and keys in a client-side encrypted vault.
17. Provide a sandbox for testing code and tasks.
18. Turn plain-language requests into reusable skills.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned source-linked assistant and administrator console with source references and unresolved questions.

Everything these tools do, in one app

• Managed OpenClaw hosting Provides a ready-to-run OpenClaw environment so users don't have to set up or manage infrastructure themselves.Found in Agent 37, Donely, JDoodleClaw
• Fast provisioning Gets a working agent environment up and running quickly, often in under a minute.Found in Agent 37, Donely, JDoodleClaw
• App integrations Connects agents to many external services like Gmail, Slack, and Notion with pre-configured OAuth flows.Found in Agent 37, Donely, OpenHuman
• Bring your own API key Lets users supply their own API keys or model accounts to control provider and billing.Found in JDoodleClaw, OpenHuman, Donely
• Local-first privacy Runs agents and stores data on the user's own machine to keep prompts and information private.Found in OpenHuman, Nut Studio, ClawApp and 1 more
• Persistent memory Retains useful context across sessions so users don't have to repeat information.Found in OpenHuman, Clawdi, Agent-Sin
• Multi-agent support Allows creating and running several specialized agents, each with its own configuration.Found in Nut Studio
• Multi-channel access Enables interacting with the agent from different surfaces like desktop, terminal, or chat apps.Found in Nut Studio, Agent-Sin
• Terminal access Provides full shell access in the browser for running custom scripts and background tasks.Found in Agent 37
• Live monitoring Shows agent activity in real time and allows in-browser chat interventions for debugging.Found in Agent 37
• Automated repair Attempts to automatically fix a broken agent instance to reduce manual maintenance.Found in Donely, Agent-Sin
• Daily backups Backs up instance data every day to protect against data loss.Found in JDoodleClaw
• No-code builder Lets users create AI agents and workflows without programming through a visual interface.Found in Pulze
• Model routing Automatically selects the most appropriate AI model for each task to improve efficiency.Found in Pulze
• Team collaboration Supports shared assistants, skills, and conversation histories with role-based access for teams.Found in Clawdi, Pulze
• Encrypted vault Stores user files and API keys with client-side encryption for added security.Found in OpenHuman, Clawdi
• Code sandbox Provides a built-in environment for testing code and tasks safely.Found in OpenHuman
• Reusable skills Turns plain-language requests into small programs that run reliably on repeat.Found in Agent-Sin

What goes in, what comes out

The workflow

1. In
   Start with

   A managed OpenClaw runtime, connected accounts, supplied API keys and stored agent context

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect a managed OpenClaw runtime

4. 3

   Connected accounts

5. 4

   Supplied API keys and stored agent context

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   A source-linked assistant and administrator console

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. One supported runtime version and a fixed set of connected services; final access, spending and production changes remain with the owner. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Agent workspace, Operations console, Admin and access. Use a list of agents with status and last activity, a large central chat and activity view, and a right-hand panel for configuration, connected accounts and memory. Let users compare runs side by side. Display running, needs attention and paused states. Provide a shared team view with comments anchored to the relevant run. Make the task-specific outcome a source-linked assistant and administrator console visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, agent versions, connected accounts, approval states, usage allowances, key rotation, backup 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

Owner-supplied API keys and model accounts, Gmail, Slack, Notion and similar services. Cloud or local storage, terminal access and chat surfaces. 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

   5 days

   One buyer segment, one recurring use case; first modules: provision a ready-to-run OpenClaw environment; start a working agent in under a minute. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

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

4. 4

   Full product

   2 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 small technical teams and solo builders running AI agents for their own workflows use it to solve "running AI agents means setting up servers, wiring integrations, storing keys and repairing broken instances by hand"?
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: Agent uptime per week and operator minutes per repaired instance.
4. Measure, then decide. Track agent uptime per week and operator minutes per repaired instance; 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 supported runtime version and a fixed set of connected services; final access, spending and production changes remain with the owner. Implement one approved input format, a bounded representative case set and the first two task modules: provision a ready-to-run OpenClaw environment; start a working agent in under a minute. Support the remaining modules with operator review: connect Gmail, Slack, Notion and similar services through pre-configured OAuth; accept the owner's own API keys and model accounts; run agents and store data on the owner's machine; retain useful context across sessions; create several specialized agents with separate configurations; reach the agent from desktop, terminal and chat surfaces; provide browser shell access for scripts and background tasks; show live activity with in-browser chat intervention; attempt automatic repair of a broken instance; back up instance data daily; build agents and workflows through a visual no-code interface; route each task to a suitable model; share assistants, skills and histories with role-based access; store files and keys in a client-side encrypted vault; provide a sandbox for testing code and tasks; turn plain-language requests into reusable skills. 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 source-linked assistant and administrator console. Retain the explicit scope boundary: One supported runtime version and a fixed set of connected services; final access, spending and production changes remain with the owner.

What the build depends on. Agent upload and preview, asynchronous provisioning jobs, editable version history, reviewer access and tested export formats. High-fidelity operations require specialist runtime QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One supported runtime version and a fixed set of connected services; final access, spending and production changes remain with the owner.

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 setup. Offer a monthly operations allowance after repeat demand. Quote complex multi-team or regulated deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked assistant and administrator console. Recurring fees must specify agent count, 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 infrastructure and maintenance work while keeping agent data and keys under the owner's control. Demonstrate a concrete source-linked assistant and administrator console using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Small technical teams and solo builders running AI agents for their own workflows professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample source-linked assistant and administrator console from a small authorized input set, with a transparent calculation of agent uptime per week and operator minutes per repaired instance and no promised savings.

The first 30 days

1. Week 1: interview five small technical teams and solo builders running AI agents for their own workflows and inspect a recent example of running AI agents means setting up servers, wiring integrations, storing keys and repairing broken instances by hand.
2. Week 2: prepare a consented or synthetic demonstration of the task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure agent uptime per week and operator minutes per repaired instance, 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: Agent uptime per week and operator minutes per repaired instance. 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

Agent uptime per week and operator minutes per repaired instance; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a source-linked assistant and administrator console. 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, connected-service settings and repair examples, together with reliable delivery for a narrow technical niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for small technical teams and solo builders running AI agents for their own workflows. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Agent 37, Donely, JDoodleClaw, OpenHuman, ClawApp, Pulze, Nut Studio, Agent-Sin and Clawdi. Compare this product with the buyer's present method on agent uptime per week and operator minutes per repaired instance. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, sandbox compute, storage, reviewer hours, client revision rounds and connected-service setup. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of a source-linked assistant and administrator console. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve owner access, source attribution, key custody and usage permissions. Owners approve substantive changes and production scope. One supported runtime version and a fixed set of connected services; final access, spending and production changes remain with the owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.