arXiv:2606.17929v1 Announce Type: new Abstract: Computer-using agents drive real software through the screen -- clicking and typing -- but they solve every task from scratch: asked to repeat a task, an agent re-reads the screen, re-reasons every tap, and pays the full cost again. We present PreAct, which lets such an agent get faster on tasks it has done before. The first time it succeeds, PreAct compiles the run into a small state-machine program-states that check the screen, transitions that act-and on later runs replays it directly instead of invoking the agent 8.5-13x faster, with no per-s
The rapid advancement in computer-using agents necessitates solutions for improving their efficiency, especially as their adoption in repetitive tasks increases.
This development addresses a critical limitation of current AI agents, significantly boosting their speed and practicality for real-world applications.
Agents will transition from solving every task from scratch to learning and optimizing repeated actions, making them far more efficient and scalable.
- · AI software developers
- · Businesses adopting automation
- · Cloud computing providers
- · Agent infrastructure platforms
- · Inefficient manual workflow processes
AI agents become significantly more efficient for repetitive computer-based tasks.
The cost-effectiveness and scalability of AI-driven automation increase, leading to broader adoption across industries.
This efficiency gain contributes to a more rapid collapse of white-collar workflows, potentially accelerating job displacement in administrative and repetitive digital roles.
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Read at arXiv cs.AI