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Computer use agents click buttons, type into fields, navigate menus, and extract data from web apps and desktop software, without an API. They perceive the screen the same way a human does: as a visual interface. That means they work on systems where no API exists, no documentation was written, and no engineer ever planned for automation.
This is for companies with legacy systems that have no API, teams that need to automate multi-step workflows across three or more tools, and anyone whose automation keeps breaking because the UI changes. If you have tried RPA and spent more time maintaining the scripts than the scripts saved you, that is the problem computer use agents solve.
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Every computer use agent engagement is scoped around one or more of these capabilities. We don't ship single-capability automations without the error-handling and escalation layers.
Playwright-based agents augmented with AI vision. They navigate web apps, extract data, fill forms, and handle dynamic content, without brittle CSS selectors.
Claude Computer Use agents that operate native desktop software. Useful for legacy Windows or macOS apps that have no API and no plans to get one.
Agents that chain actions across three or more tools (opening one app, extracting data, entering it into another, then confirming in a third) without human involvement.
The agent checks its own output at each step. If something looks wrong (unexpected modal, changed layout, missing element), it re-routes rather than proceeding on bad state.
Rather than trusting DOM signals alone, the agent takes periodic screenshots and uses vision to confirm expected state before advancing to the next action.
When the agent hits a decision it isn't confident about, it pauses, captures a screenshot, and routes the task to a human review queue. Nothing stalls silently.
Traditional RPA records exact clicks and selectors. Three scenarios where that approach fails and computer use agents hold up.
If a system has no API and no roadmap to add one, computer use is often the only option short of a full system replacement. We have built agents for internal tools from the early 2000s.
Traditional RPA breaks every time the UI updates. Someone has to go in and fix the selector. A CUA agent uses vision to find elements, so a redesign typically doesn't break the workflow.
Reading a scanned PDF inside a browser portal, classifying a screenshot, or identifying the correct row in a table that has no reliable ID attribute. These are tasks that require vision, not just DOM parsing.
An agent that completes a workflow once, on the happy path, while you watch, is a demo. An agent you can leave running is a different project. The gap is four things, and they are where most of the budget and most of the value sit.
It cannot trust its own clicks. A blind script assumes the button it clicked did what it expected. A reliable agent verifies: it takes a screenshot, confirms the screen changed the way it should, and only then advances. That check at every step is slower, and it is the single biggest reason the agent keeps working when a page loads half a second late or a modal appears that was not there yesterday.
The unhappy paths are most of the work. Sessions expire, network calls hang, an unexpected dialog steals focus, a record is missing. The demo never shows these; production is mostly these. We build the recovery logic deliberately: detect the off-script state, retry within bounds, and route to a human with a screenshot rather than charging ahead on a broken assumption.
Irreversible actions need a gate. Reading data and submitting a payment are not the same risk, and an agent that treats them alike is dangerous. We mark the consequential actions, require a confirmation step or a human approval before they fire, and keep an action log so every step the agent took is auditable after the fact. You should be able to describe the worst thing the agent can do in one sentence.
Drift is real, so monitoring is not optional. Even a vision-based agent can be thrown by a significant redesign of the target software. We instrument task-completion rate and alert when it drops, so a change on the far side of a system you do not control surfaces as a notification rather than a silent backlog of failed work. The agent is resilient, not immortal, and we are honest about the difference.
Computer use agents are powerful but not the right tool for every automation. Being honest about that saves everyone time.
Apps that have a working API
If the target system has an API, use the API. It is 10× faster, more reliable, and cheaper to run than a visual agent. We will tell you this in discovery and redirect you to the right approach.
Simple single-step automations under 5 clicks
A five-click workflow that runs once a day doesn't need an AI agent. It needs a macro or a simple Playwright script. We won't bill you a heavyweight engagement for a task that takes a developer two hours.
Workflows needing sub-second latency
Computer use agents operate on a 1–3 second action loop: each step takes a screenshot, reasons about it, then acts. If your workflow needs to complete in under a second or handle thousands of concurrent sessions, this is the wrong architecture.
We choose tools based on what the workflow requires. We benchmark before we commit.