AI app for it and development · no coding needed
Local agent operations control portal
Reduce tool sprawl while keeping agent execution and data on the team's own machines.
Made for: IT and development teams running AI agents on their own machines to automate tasks and control apps
What it does for you
The problem
Agent work is scattered across several rented tools, so execution, memory, approvals and app control do not sit in one owned place.
What it gives you
A reviewed, reversible record of agent actions
What you give it
Local modelsdesktop appsfilesterminalsapproved integrations
Build your own version of Osaurus, Moltbot and more
One app with what these 9 AI tools do, yours to keep and change: Osaurus, Moltbot, Munder Difflin, Local Operator, Cua, The Factory Desktop App, Sidekick™, Vy by Vercept, Maestri.
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
How it works, step by step
- Run the agent and its data on the local machine
- Keep memory and context across sessions
- Operate desktop apps, browser, terminal and files
- Show intended actions and require confirmation before acting
- Run several agents and coordinate their work
- Write and run code in a sandbox
- Accept plain-language instructions
- Reach the agent through chat apps
- Connect external apps and services
- Provide a spatial canvas for terminals, notes and sketches
- Let agents delegate tasks to each other
- Select the model that balances cost and performance
- Run scheduled and background tasks
- Revert actions the agent took
- Remember installed packages, repositories and credentials across sessions
- Produce diagrams, charts and dashboards
- See and understand what is on the screen
- Show real-time analytics about workflows and tasks
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewed, reversible record of agent actions with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Local agent operations control portal with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Local agent operations control portal with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria13 KB
- demo/index.htmlThe working demo on sample data197 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce tool sprawl while keeping agent execution and data on the team's own machines. For IT and development teams running AI agents on their own machines to automate tasks and control apps, convert local models, desktop apps, files, terminals and approved integrations into a reviewed, reversible record of agent actions. The benefit is a testable hypothesis, measured through approved agent actions per operator hour and reverted actions after review; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect local models, desktop apps, files, terminals and approved integrations, then follow this sequence: 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. Resolve uncertain cases with qualified reviewers, approve a reviewed, reversible record of agent actions, and measure approved agent actions per operator hour and reverted actions after review against a documented baseline.
How the AI works
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.
Safeguards
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.
What to build first
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.
What it can connect to
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.
The screens in detail
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.
