Cloud computers for AI agents
A cloud computer for AI gives software a place to act: a browser to use websites, a shell to run commands, and files to read and produce. Pine Computer combines that environment with a built-in agent and a desktop a person can watch or take over. Developers can also drive it with their own agent through the API.
By Pine AI · Published
Choose the environment for the work
Start with the job’s inputs and outputs. A task that reads a page and returns a few fields may need only a browser. A task that downloads invoices, compares them with a spreadsheet and returns a workbook also needs file handling and a place to run code. A task that pauses for a person to sign in needs a way to share control.
For example, consider a supplier-invoice workflow. Your application supplies the supplier list and purchase orders. The computer opens each portal, downloads the available invoices, compares the records and returns files for review. This is an example of a workflow to build, not a claim that every supplier portal will work without intervention.
Pine’s published HR-report example makes the browser-to-file boundary concrete: the application supplies a reports.csv manifest and a check script, the agent exports the listed reports from the portal, and the shell runs the check before the application downloads a zip. For your own workflow, make that manifest the acceptance list: check that every requested report is present, readable and for the requested period before marking the job complete.
Separate the computer from the agent
The computer is the working environment. The agent decides what to do in it. With Pine’s built-in agent, your application supplies a goal and handles progress, results and requests for input. With your own agent, your code chooses and issues the actions through the API.
The two paths do not expose identical perception capabilities. Pine’s built-in agent reads browser pages as structure and receives changes. The documented bring-your-own-agent path uses screenshots today; do not assume the same structured browser view is available to every API client. Native desktop application automation is a separate roadmap item.
Plan what persists between jobs
In Pine Computer, a computer can keep sign-ins and learned skills between jobs. A session is the unit of work on that computer, with its own browser window, shell and working files. Sharing a computer also means sharing its sign-in context, so choose that boundary deliberately for each customer or account.
Saved sign-ins do not mean a permanent working disk. Tabs, working files and sessions are temporary; your application should retrieve the output files it needs before ending the work. Restoration is best-effort during private beta. An expired login can still require a person to sign in again.
The saved-sign-in recording uses two programs on the same computer: a person answers Amazon’s verification-code request in the first, and the second adds an item to the cart after the computer has been stopped and brought back. Keep the customer-to-computer association in your application so the next job resumes the intended account context.
Give each customer a clear computer boundary
A computer per customer makes the account boundary explicit when several jobs run at once. In Pine’s published bookkeeping demonstration, five client workflows each receive their own computer, statement file and sign-in. Each returns a report, and the program stops the computers when the work ends, including when an error interrupts it.
That recording uses invented companies and books on a Pine-operated Akaunting instance. It shows the application pattern, not customer accounting results or a capacity benchmark. In your integration, associate every input, output and failure with its customer, and keep cleanup independent of whether the agent reports success.
Evaluate a complete run
Test with a representative job before expanding to many computers. Define what a correct result looks like, inspect the returned artifact and compare it with the agent’s final message. Keep records of interventions and incomplete runs alongside successful ones.
- Name the inputs, permitted accounts and expected output files.
- Choose Pine’s built-in agent or your own action loop.
- Decide when a person must sign in, clarify a request or approve an action.
- Check the result against the original request and retain its source evidence.
- Retrieve the output files and record failures before ending the session.
Where Pine Computer fits
Pine Computer is made by Pine AI and is in private beta. It provides browser, shell and file work, parallel computers and an embeddable live desktop. It is unrelated to PINE64 hardware. Evaluate it when your product needs to carry out work across websites and files and let a person step in along the way.
The developer documentation is the source for current SDK methods and lifecycle behavior. The examples below show the kinds of work this environment can support; they are not measured customer outcomes or completion guarantees.
Explore the workflow
These use cases are examples, not customer case studies.
- Post supplier invoices: Your payables software pulls each invoice off the supplier’s portal and matches every line to the purchase order.
- Research a market: An analyst names a market and gets a report that ties every figure to its source.
- Test a web app: Your users write test steps in plain English, and each run follows them in a real browser and screenshots every step.
Product documentation
Use the developer docs for current API contracts and integration instructions.