Local agent operations control portal

For

IT and development teams running AI agents on their own machines to automate tasks and control apps

Solves

Agent work is scattered across several rented tools, so execution, memory, approvals and app control do not sit in one owned place.

Delivers

A reviewed, reversible record of agent actions

Built in

about 5 weeks of creation time, MVP in 6 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 tool sprawl while keeping agent execution and data on the team's own machines.

1. Run the agent and its data on the local machine.
2. Keep memory and context across sessions.
3. Operate desktop apps, browser, terminal and files.
4. Show intended actions and require confirmation before acting.
5. Run several agents and coordinate their work.
6. Write and run code in a sandbox.
7. Accept plain-language instructions.
8. Reach the agent through chat apps.
9. Connect external apps and services.
10. Provide a spatial canvas for terminals, notes and sketches.
11. Let agents delegate tasks to each other.
12. Select the model that balances cost and performance.
13. Run scheduled and background tasks.
14. Revert actions the agent took.
15. Remember installed packages, repositories and credentials across sessions.
16. Produce diagrams, charts and dashboards.
17. See and understand what is on the screen.
18. Show real-time analytics about workflows and tasks.
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 reviewed, reversible record of agent actions with source references and unresolved questions.

Everything these tools do, in one app

• Local on-device execution Runs the AI agent and its data on your own machine instead of a remote server.Found in Osaurus, Moltbot, Munder Difflin and 4 more
• Persistent memory and context Keeps memory and context across sessions so the agent remembers earlier work.Found in Osaurus, Moltbot, Local Operator and 2 more
• Desktop and app control Lets the agent operate your desktop apps, browser, terminal, and files to carry out tasks.Found in Osaurus, Moltbot, The Factory Desktop App and 2 more
• Approval and confirmation gate Shows what the agent intends to do and requires your confirmation before it acts.Found in Osaurus, Sidekick™
• Multi-agent orchestration Runs several agents at once and coordinates their work on tasks.Found in Munder Difflin, Local Operator, The Factory Desktop App and 1 more
• Code execution sandbox Writes and runs code in a sandbox to solve problems and produce files.Found in Osaurus, Local Operator
• Natural language commands Lets you give instructions in plain language instead of clicking through menus.Found in Sidekick™, Vy by Vercept
• Chat app access Lets you reach the agent through familiar chat apps like WhatsApp, Telegram, Signal, or Slack.Found in Moltbot
• Third-party integrations Connects to external apps and services to exchange data and trigger actions.Found in Moltbot, Cua
• Visual workspace canvas Provides a spatial canvas where terminals, notes, and sketches live as movable nodes.Found in Maestri
• Agent-to-agent communication Lets agents talk to each other and delegate tasks based on expertise.Found in Local Operator, Maestri
• Automatic model selection Picks which AI model to use to balance cost and performance.Found in Local Operator
• Scheduled and background tasks Runs tasks proactively in the background or on a recurring schedule.Found in Local Operator
• Undo for agent actions Reverts actions the agent took, such as reorganizing your desktop.Found in Sidekick™
• Persistent machines Remembers installed packages, repositories, and credentials across sessions.Found in The Factory Desktop App
• Visual outputs and diagrams Produces diagrams, charts, and dashboards so agent work is observable.Found in The Factory Desktop App
• Screen understanding Sees and understands what is on your screen to act on it.Found in Vy by Vercept
• Analytics dashboard Shows real-time insights about your workflows and tasks.Found in Cua

What goes in, what comes out

The workflow

1. In
   Start with

   Local models, desktop apps, files, terminals and approved integrations

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect local models

4. 3

   Desktop apps

5. 4

   Files

6. 5

   Terminals and approved integrations

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   A reviewed, reversible record of agent actions

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. Local model execution on the operator's own machine; final approval and consequential actions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Agent workspace and canvas, Approval and action log, Local machine and integration settings. Use a node canvas for agents, terminals and notes, a left panel for machines and sessions, and a right panel for approvals, memory and analytics. Let users compare planned and completed actions side by side. Display running, awaiting approval and reverted states. Provide a client preview link with comments anchored to the relevant action. Make the task-specific outcome a reviewed, reversible record of agent actions visible beside its evidence, review state and value baseline.

Accounts and administration

Machine ownership, session versions, operator comments, approval states, usage allowances, action 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

Operator-owned machines, local models, desktop apps, terminals and permitted chat apps. 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

   6 days

   One buyer segment, one recurring use case; first modules: run the agent and its data on the local machine; keep memory and context across sessions. 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 IT and development teams running AI agents on their own machines to automate tasks and control apps use it to solve "agent work is scattered across several rented tools, so execution, memory, approvals and app control do not sit in one owned place"?
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: Approved agent actions per operator hour and reverted actions after review.
4. Measure, then decide. Track approved agent actions per operator hour and reverted actions after review; 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 local machine profile and one approved integration set; final approval and consequential actions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: run the agent and its data on the local machine; keep memory and context across sessions. Support the third module with operator review: operate desktop apps, browser, terminal and files. 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 reviewed, reversible record of agent actions. Retain the explicit scope boundary: One local machine profile and one approved integration set; final approval and consequential actions remain human.

What the build depends on. Machine profile setup, asynchronous agent jobs, editable action history, reviewer access and tested export formats. High-fidelity production requires specialist IT QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One local machine profile and one approved integration set; final approval and consequential actions 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 machine profile. Offer a monthly production allowance after repeat demand. Quote complex multi-machine or specialist integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, reversible record of agent actions. 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 tool sprawl while keeping agent execution and data on the team's own machines. Demonstrate a concrete reviewed, reversible record of agent actions using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

IT and development teams running AI agents on their own machines professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, reversible record of agent actions from a small authorized input set, with a transparent calculation of approved agent actions per operator hour and reverted actions after review and no promised savings.

The first 30 days

1. Week 1: interview five IT and development teams running AI agents on their own machines and inspect a recent example of agent work scattered across several rented tools.
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 approved agent actions per operator hour and reverted actions after review, 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: Approved agent actions per operator hour and reverted actions after review. 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

Approved agent actions per operator hour and reverted actions after review; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a reviewed, reversible record of agent actions. 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 actions, machine profiles and review examples, together with reliable delivery for a narrow IT operations niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for IT and development teams running AI agents on their own machines to automate tasks and control apps. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Osaurus, Moltbot, Munder Difflin, Local Operator, Cua, The Factory Desktop App, Sidekick™, Vy by Vercept and Maestri. Compare this product with the buyer's present method on approved agent actions per operator hour and reverted actions after review. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Local model runs, sandbox compute, storage, reviewer hours, operator revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of a reviewed, reversible record of agent actions. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve operator intent, source attribution, action accuracy and usage permissions. Operators approve substantive changes and external action scope. One local machine profile and one approved integration set; final approval and consequential actions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.