AI agents can increasingly use software by looking at its screen and operating its mouse and keyboard. That makes the graphical interface a new route into applications—including some that lack a suitable API—but it does not make APIs obsolete. Computer use is an additional, more flexible interaction layer, with reliability and safety that must be assessed for each task.
What it means for a computer to become an API
A traditional API gives software a defined way to request data or actions from another system. A computer-use agent instead interacts through the interface a person sees: it observes a screen, reasons about what is displayed, and takes actions such as clicking, typing, or scrolling. It then observes the result and continues in a perception, reasoning, and action loop.
OpenAI described its Computer-Using Agent (CUA) in 2025 as using screenshot observations and virtual mouse and keyboard actions, without requiring specialized agent-friendly APIs. Microsoft Foundry’s preview announcement describes browser and desktop automation, including work with older desktop applications. These are vendor-described capabilities and use cases, not evidence that every workflow is dependable in production. OpenAI’s CUA announcement and Microsoft Foundry’s Computer Use preview announcement explain their respective approaches.
The term “computer use” covers more than one design. Systems differ in the environments they operate in, the observations and actions available to them, and how the agent is built. A 2026 survey organizes the field around these dimensions rather than treating computer-use agents as one uniform technology. The survey of agents for computer use provides that broader technical framing.
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When screen interaction helps—and when an API is better
Computer use is most useful when a task depends on an application with no suitable API, or when the work crosses browser and desktop interfaces built for people. It can extend automation to older software and to combinations of services that otherwise lack a shared integration.
A structured API remains preferable when it provides the needed operation: its defined interface is a more direct way to request that operation than interpreting and manipulating a visual screen. Computer use broadens the available reach; it does not replace structured interfaces. A hybrid system can use APIs for tasks they cover and resort to screen interaction for the remaining visual or legacy steps.
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| Approach | How it interacts | Best fit | Important trade-off |
|---|---|---|---|
| Structured API | Calls a defined software interface | A supported operation with a suitable API | Only covers operations and systems exposed through that interface |
| Computer use | Observes a screen and uses mouse or keyboard actions | Visual workflows, legacy applications, or tasks without a suitable API | Must interpret a changing interface and may take an unintended action |
| Hybrid | Combines API calls with screen interaction | Workflows where some steps have suitable APIs and others do not | Requires choosing and coordinating the interaction method for each step |
OpenAI’s description of CUA emphasizes reaching the “long tail” of digital use cases beyond specialized agent-friendly APIs. That is a statement of the technology’s aim, not a guarantee that every application or workflow will work. OpenAI’s announcement describes the proposition.
What published benchmark results show—and what they do not
Computer-use scores measure performance on particular task sets, not general reliability across real workplaces. Results from different benchmark suites should not be ranked as if they measured the same tasks. The figures below are reported by the named organizations and retain their benchmark and sample context.
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| Reported result | What it measures and who reported it |
|---|---|
| 38.1% on OSWorld; 58.1% on WebArena; 87% on WebVoyager | Results OpenAI reported for CUA in its January 23, 2025 announcement. These are three different benchmarks and task settings, not one general accuracy rate. OpenAI, “Computer-Using Agent”. |
| 63% for Fara1.5-9B; 57% for Fara1.5-4B; 72% for Fara1.5-27B | Microsoft Research’s 2026 vendor-reported task-success results on Online-Mind2Web’s 300 tasks across 136 websites. The figures apply to this benchmark and model family. Microsoft Research, Fara1.5. |
| 80.8% average blind goal-directedness rate across nine evaluated models | Microsoft Research’s result on the 90-task BLIND-ACT benchmark. It concerns the benchmark’s defined risky behavior patterns; it is not the share of all computer-use actions that fail. Microsoft Research, “Just Do It!?”. |
| 93.75% agreement with human annotations | The agreement reported for BLIND-ACT’s LLM-based judges, not an agent task-success rate. Microsoft Research, “Just Do It!?”. |
For a procurement or deployment decision, test the intended applications and tasks directly. Compare task success, recovery from interface changes, latency and cost, human approval controls, credential and data isolation, and the quality of safety evaluation. Record the benchmark name, task set, date, and whether a result comes from a vendor or an independent evaluator. Anthropic’s testing and discussion of token use and effort trade-offs are its own vendor results, not neutral comparisons across providers. Anthropic’s computer- and browser-use guidance describes its testing approach.
Why pursuing a goal can still be unsafe
An agent can focus on completing the requested goal rather than first resolving whether the instruction is clear, feasible, safe, or appropriate in context. Microsoft Research’s BLIND-ACT work identifies patterns such as weak contextual reasoning, assumptions under ambiguity, and pursuit of contradictory or infeasible goals. Its authors report that prompting interventions reduce the observed behavior but leave substantial risk. The BLIND-ACT publication describes the benchmark and findings.
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Security concerns also extend beyond the model. The MIT AI Agent Index’s 2026 review of its defined 30-agent sample found known incidents or reported security concerns for 8 of 30 indexed agents, and documented prompt-injection vulnerabilities for 2 of 5 browser agents. The index found that 25 of 30 agents disclosed no internal safety results and 23 of 30 had no third-party testing information. Those disclosure findings do not establish that an organization did no internal safety work; they describe what the index could find in public documentation. The MIT AI Agent Index sets out its sample and review.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to deploy computer-use agents more safely
Use isolation, narrow permissions, and human review as controls around an agent—not as guarantees that it cannot make mistakes.
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- Cool Comfort: Enjoy a stable surface with our lap desk's dual bolster cushion, designed for comfort and airflow, keeping your lap cool during extended use.
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- Constrain the environment. Microsoft Foundry recommends running computer use only on low-privilege virtual machines that do not contain sensitive data or credentials. Microsoft Foundry’s preview guidance states this recommendation.
- Limit access. Keep credentials and sensitive information out of the environment the agent can inspect or operate, and grant only the access its task needs. This follows the low-privilege, no-sensitive-data guidance rather than relying on the model to avoid exposed information.
- Require approval for consequential steps. OpenAI’s CUA announcement describes confirmation for sensitive steps such as entering login details or responding to CAPTCHA forms. Microsoft Foundry’s preview describes warnings for malicious instructions or sensitive domains and human acknowledgment. Such gates are deployment controls; they do not prove an action is safe. OpenAI’s announcement and Microsoft Foundry’s guidance describe these preview-era controls.
- Evaluate the complete setup. Assess the model together with the browser or desktop tool, its permissions, the data it can reach, approval steps, and recovery behavior. A strong capability score by itself does not establish that the surrounding system is secure or suitable for a consequential workflow.
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