Microsoft is expanding agentic AI on personal computers with Fara 7B, a compact computer use agent model that can complete complex tasks entirely on a local device. The experimental release is designed to collect feedback and offers enterprises an early look at how AI agents may run sensitive workflows without sending data to the cloud. Microsoft said the model can also match or exceed the performance of larger systems like GPT 4o in real user interface navigation tasks.
In a blog post, Microsoft explained that “Unlike traditional chat models that generate text based responses, Computer Use Agent models like Fara 7B leverage computer interfaces, such as a mouse and keyboard, to complete tasks on behalf of users.” The company added that “With only 7 billion parameters, Fara 7B achieves state of the art performance within its size class and is competitive with larger, more resource intensive agentic systems that depend on prompting multiple large models.”
Fara 7B processes screenshots and interprets visual elements at the pixel level. This allows the agent to navigate interfaces even when the code behind them is complex or unavailable.
Internal tests showed strong results. The model recorded a success rate of 73.5 percent on the WebVoyager test, outperforming GPT 4o when both acted as computer use agents. Microsoft noted that Fara 7B usually finishes tasks in fewer steps than earlier models of a similar size, which could help deliver faster and more consistent desktop automation.
The company has also added a safeguard called Critical Points. This feature forces the agent to pause and ask for user approval before it performs irreversible actions such as sending emails or completing financial transactions.
Industry analysts say that the shift towards compact, local models like Fara 7B reflects a wider change in enterprise AI strategies. Cloud systems remain dominant for large scale reasoning and organisation wide search, but many everyday workflows involve moving data between internal applications on a laptop, where privacy demands that the information stays on the device.
“Edge based models solve three big problems with cloud AI: compute cost, data leaving the device, and latency,” said Pareekh Jain, CEO of Pareekh Consulting. He added that most enterprise tasks take place across internal apps on a laptop, making a local agent a better option.
Charlie Dai, vice president and principal analyst at Forrester, said Fara 7B shows how lightweight, device based agents will become more important as organisations increase their use of agentic AI. Dai said, “For enterprises, this signals a gradual decentralisation of AI workloads, lowering dependency on hyperscale infrastructure while demanding new strategies for edge governance and model lifecycle management.”
Tulika Sheel, senior vice president at Kadence International, said the trend highlights a move towards hybrid AI structures. Local agents can manage privacy sensitive workflows, while cloud systems continue to offer scale.
By keeping data on the device and reducing reliance on external compute, small on device agents provide a practical way to automate sensitive and repetitive tasks without exposing information to outside systems.
However, these pixel level agents also raise practical challenges. Jain compared this method to an AI powered version of robotic process automation, where the agent copies mouse and keyboard actions to move data between systems.
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