Practical Considerations for Agentic LLM Systems

TL;DR

This paper provides practical insights and considerations for designing and deploying robust agentic LLM systems in real-world applications, addressing challenges like unpredictability and resource management.

Contribution

It categorizes research findings into Planning, Memory, Tools, and Control Flow, offering actionable guidance for building effective LLM agents.

Findings

Highlights practical design considerations for LLM agents.

Emphasizes managing stochasticity and resource efficiency.

Provides a framework for future research and deployment.

Abstract

As the strength of Large Language Models (LLMs) has grown over recent years, so too has interest in their use as the underlying models for autonomous agents. Although LLMs demonstrate emergent abilities and broad expertise across natural language domains, their inherent unpredictability makes the implementation of LLM agents challenging, resulting in a gap between related research and the real-world implementation of such systems. To bridge this gap, this paper frames actionable insights and considerations from the research community in the context of established application paradigms to enable the construction and facilitate the informed deployment of robust LLM agents. Namely, we position relevant research findings into four broad categories--Planning, Memory, Tools, and Control Flow--based on common practices in application-focused literature and highlight practical considerations to make when designing agentic LLMs for real-world applications, such as handling stochasticity and managing resources efficiently. While we do not conduct empirical evaluations, we do provide the necessary background for discussing critical aspects of agentic LLM designs, both in academia and industry.