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  • AI Computer Use in 2026: What I Automate and What I Keep Manual

AI Computer Use in 2026: What I Automate and What I Keep Manual

Nabil Samari
Nabil Samari
March 25, 20264 min read
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AI Computer Use in 2026: What I Automate and What I Keep Manual

AI computer use is no longer a laboratory demo. Modern agents can inspect a browser or desktop, click controls, fill forms, move through applications and verify whether an action worked.

That sounds like full automation. In practice, the useful version is closer to careful collaboration: the agent handles repetitive navigation and evidence gathering while a person keeps control of decisions, credentials and consequences.

I now use this approach through Hermes and other computer-use tools. The technology has improved significantly, but the most important lesson is not how many clicks an agent can make. It is knowing which clicks it should never make alone.

A real workflow instead of a magic demo

A good example is website quality assurance. I can ask an agent to open a staging site, inspect desktop and mobile layouts, follow navigation, check buttons, record console errors and compare the result with the brief.

The agent can gather the evidence and even prepare a code fix. I still review the scope, run the tests and verify production after deployment. That division of work is far more useful than handing over an open-ended instruction such as “fix the website”.

The same pattern works for research and administration:

  • collect public information from several pages
  • compare a live system with a checklist
  • prepare drafts without publishing them
  • move data between tools when the mapping is explicit
  • monitor a long process and report the actual result

The common thread is clear: the task has a defined goal, observable state and a way to verify success.

Where computer use helps most

Computer control is valuable when a service has no practical API, when the browser view itself must be checked, or when a workflow spans several applications.

It is especially useful for visual QA. A normal HTTP test can confirm that a page returns status 200, but it cannot tell whether a mobile menu covers the heading or whether a button has moved outside the viewport. An agent that can inspect the rendered page can catch those problems and save screenshots as evidence.

It also helps with repetitive back-office work. Downloading a known report, copying a value into a prepared spreadsheet or checking the status of several records can be reasonable tasks when the rules are precise.

Nabil Samari

An events, sales and marketing professional building digital services and local AI solutions.

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      What I do not automate blindly

      Access to a mouse and keyboard is access to consequences. A browser session may contain email, customer data, invoices, advertising accounts and admin tools.

      My boundary is simple: passwords, payment details, permission dialogs and other secrets stay with the person. Sending messages, publishing content, deleting data, making purchases and changing production settings need human approval before the final action.

      This is not a limitation to work around. It is the safety model.

      The agent should also ignore instructions embedded in webpages, documents and screenshots. A page is data, not a trusted operator. Otherwise a malicious line of text can redirect the workflow away from the task the user actually requested.

      Computer use still fails in ordinary ways

      Interfaces change. Buttons move. Cookie banners cover controls. Accessibility labels are missing. A slow network makes the page look ready before the data has loaded.

      That is why reliable automation cannot be a long chain of blind clicks. After every meaningful action, the agent should inspect the new state. If an element is missing, it should stop, gather evidence and choose another safe route instead of guessing coordinates indefinitely.

      Long tasks also need proper process handling. A build or deployment should run as a tracked background process, and completion should be verified from its real output. “I started it” is not the same as “it succeeded”.

      A practical way to begin

      Start with one low-risk workflow that you already understand well.

      1. Write down the expected starting state and final state.
      2. Separate read-only steps from actions that change data.
      3. Decide which final actions require human approval.
      4. Require evidence: a status code, test output, screenshot, record ID or a read-back from the destination.
      5. Keep a recovery route for every file move or configuration change.

      Only after that workflow works reliably should you expand it.

      The useful future is supervised

      The interesting shift is not that AI can imitate a person moving a mouse. The shift is that software can now work across tools that were never designed to connect.

      The best results still come from clear boundaries: let the agent do the patient navigation, comparison and verification, but keep authority over identity, money, communication and irreversible changes.

      That is less dramatic than “AI runs my computer”. It is also much closer to how useful automation works in real life.

      Read next: I built my own AI team and AI did not reduce my work.

      #AI#Automation#Computer use#Hermes#Security

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      AI Didn't Reduce My Work. It Increased It. And That's a Good Thing.
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      Using AI agents did not reduce my workload. It changed what I can build, increased my ambition and made verification more important than ever.

      I Built My Own AI Team — And It Costs Less Than My Wolt+ Subscription
      I Built My Own AI Team — And It Costs Less Than My Wolt+ Subscription

      How I assembled a practical team of specialised AI agents, divided their responsibilities and kept human review in every consequential action.

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