arXiv:2605.21470v1 Announce Type: new Abstract: Computer-use agents (CUA) automate tasks specified with natural language such as "order the cheapest item from Taco Bell" by generating sequences of calls to tools such as click, type, and scroll on a browser. Current implementations follow a sequential fetch-screenshot-execute loop where each iteration requires an LLM call, resulting in high latency and frequent errors from incorrect tool use. We present agent just-in-time (JIT) compilation, an alternative that compiles task descriptions directly into executable code that is free to include LLM
The paper addresses current limitations in agentic systems' performance, specifically latency and error rates, which are key bottlenecks for broader adoption and utility.
This development proposes a method to significantly improve the efficiency and reliability of AI agents, making them more practical for complex, real-world tasks and potentially accelerating their integration into various sectors.
The shift from sequential LLM calls to JIT compilation fundamentally alters how AI agents process tasks, promising lower latency and greater autonomy.
- · AI Agent developers
- · SaaS companies leveraging agents
- · E-commerce platforms
- · Users of automation software
- · Inefficient automation software providers
- · Companies reliant on high-latency AI processes
AI agents become significantly faster and more reliable in executing tasks.
Increased adoption of AI agents across various industries due to improved performance and reduced operational costs.
The development of more sophisticated and general-purpose autonomous agents, capable of handling highly complex, multi-step workflows with minimal human oversight.
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Read at arXiv cs.LG