Fundamentals of Building Autonomous LLM Agents
It addresses the need for more autonomous and intelligent AI systems for applications requiring human-like cognitive processes, but it is incremental as it reviews and integrates existing methods.
This paper tackles the problem of traditional LLMs being limited in real-world tasks by exploring architectures to build autonomous LLM agents that can automate complex tasks and bridge the performance gap with human capabilities, showing that integrating perception, reasoning, memory, and execution systems leads to more capable and generalized software bots.
This paper reviews the architecture and implementation methods of agents powered by large language models (LLMs). Motivated by the limitations of traditional LLMs in real-world tasks, the research aims to explore patterns to develop "agentic" LLMs that can automate complex tasks and bridge the performance gap with human capabilities. Key components include a perception system that converts environmental percepts into meaningful representations; a reasoning system that formulates plans, adapts to feedback, and evaluates actions through different techniques like Chain-of-Thought and Tree-of-Thought; a memory system that retains knowledge through both short-term and long-term mechanisms; and an execution system that translates internal decisions into concrete actions. This paper shows how integrating these systems leads to more capable and generalized software bots that mimic human cognitive processes for autonomous and intelligent behavior.