Fundamentals of Building Autonomous LLM Agents

2025-10-10

AI Summary

Traditional large language models (LLMs) lack autonomy and sustained interactive capability, limiting their effectiveness in complex real-world tasks. To address this, we propose an agentive LLM architecture that integrates environment perception, multi-step reasoning (Chain-of-Thought and Tree-of-Thought), hierarchical short- and long-term memory mechanisms, and action execution interfaces—enabling end-to-end autonomous decision-making loops. Unlike passive, reactive LLMs, our architecture supports proactive goal decomposition, dynamic state maintenance, and adaptive behavior driven by environmental feedback. Experimental results demonstrate substantial improvements in task completion rates and generalization across diverse benchmarks, significantly narrowing the performance gap with human baselines. The framework provides a scalable, unified foundation for advancing LLMs toward embodied, persistent agency.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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.

Problem

• Developing autonomous LLM agents to overcome traditional model limitations
• Creating systems for perception, reasoning, memory, and execution integration
• Building agents that automate complex tasks and mimic human cognition

Innovation

• Perception system converts environmental data into representations
• Reasoning system uses Chain-of-Thought for planning
• Memory system combines short-term and long-term mechanisms