Building LLM Agents by Incorporating Insights from Computer Systems
This work provides foundational insights for researchers and developers aiming to build more robust and scalable LLM agents, though it is incremental in applying existing computer system concepts to this domain.
The paper addresses the lack of systematic design principles in LLM-driven autonomous agents by advocating for a structured framework inspired by computer systems, particularly the von Neumann architecture, to improve generality and scalability.
LLM-driven autonomous agents have emerged as a promising direction in recent years. However, many of these LLM agents are designed empirically or based on intuition, often lacking systematic design principles, which results in diverse agent structures with limited generality and scalability. In this paper, we advocate for building LLM agents by incorporating insights from computer systems. Inspired by the von Neumann architecture, we propose a structured framework for LLM agentic systems, emphasizing modular design and universal principles. Specifically, this paper first provides a comprehensive review of LLM agents from the computer system perspective, then identifies key challenges and future directions inspired by computer system design, and finally explores the learning mechanisms for LLM agents beyond the computer system. The insights gained from this comparative analysis offer a foundation for systematic LLM agent design and advancement.