CoAct-1: Computer-using Agents with Coding as Actions
Autonomous agents that operate computers via Graphical User Interfaces (GUIs)
often struggle with efficiency and reliability on complex, long-horizon tasks.
While augmenting these agents with planners can improve task decomposition,
they remain constrained by the inherent limitations of performing all actions
through GUI manipulation, leading to brittleness and inefficiency. In this
work, we introduce a more robust and flexible paradigm: enabling agents to use
coding as a enhanced action. We present CoAct-1, a novel multi-agent system
that synergistically combines GUI-based control with direct programmatic
execution. CoAct-1 features an Orchestrator that dynamically delegates subtasks
to either a conventional GUI Operator or a specialized Programmer agent, which
can write and execute Python or Bash scripts. This hybrid approach allows the
agent to bypass inefficient GUI action sequences for tasks like file management
and data processing, while still leveraging visual interaction when necessary.
We evaluate our system on the challenging OSWorld benchmark, where CoAct-1
achieves a new state-of-the-art success rate of 60.76%, significantly
outperforming prior methods. Furthermore, our approach dramatically improves
efficiency, reducing the average number of steps required to complete a task to
just 10.15, compared to 15 for leading GUI agents. Our results demonstrate that
integrating coding as a core action provides a more powerful, efficient, and
scalable path toward generalized computer automation.
689452e1741a16f544fbd009
Linxin Song
claimed_verified
2025-08-08T16:21:42.723Z
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Linxin Song
user
linxinso
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Yutong Dai
689452e1741a16f544fbd00b
Viraj Prabhu
689452e1741a16f544fbd00c
Jieyu Zhang
689452e1741a16f544fbd00d
Taiwei Shi
claimed_verified
2025-08-19T10:50:21.520Z
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Taiwei Shi
user
MaksimSTW
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Li Li
689452e1741a16f544fbd00f
Junnan Li
689452e1741a16f544fbd010
Silvio Savarese
689452e1741a16f544fbd011
Zeyuan Chen
689452e1741a16f544fbd012
Jieyu Zhao
689452e1741a16f544fbd013
Ran Xu
689452e1741a16f544fbd014
Caiming Xiong
CoAct-1: Computer-using Agents with Coding as Actions
Autonomous agents that operate computers via Graphical User Interfaces (GUIs)
often struggle with efficiency and reliability on complex, long-horizon tasks.
While augmenting these agents with planners can improve task decomposition,
they remain constrained by the inherent limitations of performing all actions
through GUI manipulation, leading to brittleness and inefficiency. In this
work, we introduce a more robust and flexible paradigm: enabling agents to use
coding as a enhanced action. We present CoAct-1, a novel multi-agent system
that synergistically combines GUI-based control with direct programmatic
execution. CoAct-1 features an Orchestrator that dynamically delegates subtasks
to either a conventional GUI Operator or a specialized Programmer agent, which
can write and execute Python or Bash scripts. This hybrid approach allows the
agent to bypass inefficient GUI action sequences for tasks like file management
and data processing, while still leveraging visual interaction when necessary.
We evaluate our system on the challenging OSWorld benchmark, where CoAct-1
achieves a new state-of-the-art success rate of 60.76%, significantly
outperforming prior methods. Furthermore, our approach dramatically improves
efficiency, reducing the average number of steps required to complete a task to
just 10.15, compared to 15 for leading GUI agents. Our results demonstrate that
integrating coding as a core action provides a more powerful, efficient, and
scalable path toward generalized computer automation.
A multi-agent system that combines GUI control with programmatic execution improves efficiency and success in complex computer automation tasks.
Graphical User Interfaces (GUIs) planners task decomposition coding CoAct-1 Orchestrator GUI Operator Programmer agent OSWorld benchmark success rate efficiency steps computer automation