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Exploring Autonomous Agents through the Lens of Large Language Models: A Review

Saikat Barua

TL;DR

This review examines the integration of Large Language Models (LLMs) into autonomous agents, detailing transformer foundations, memory/planning/action architectures, and diverse prompting strategies. It surveys how tools and grounding (RAG, APIs) mitigate limitations like hallucinations and enable real-world task execution, supported by evaluation platforms such as AgentBench, WebArena, and ToolLLM. The paper highlights current performance gaps, implementation constraints, and methods to improve alignment, multimodality, and agent ecosystems, offering a roadmap toward robust, real-world autonomous agents. Collectively, the work underscores the potential of LLM-driven agents across domains while acknowledging challenges that require continued research and practical evaluation frameworks.

Abstract

Large Language Models (LLMs) are transforming artificial intelligence, enabling autonomous agents to perform diverse tasks across various domains. These agents, proficient in human-like text comprehension and generation, have the potential to revolutionize sectors from customer service to healthcare. However, they face challenges such as multimodality, human value alignment, hallucinations, and evaluation. Techniques like prompting, reasoning, tool utilization, and in-context learning are being explored to enhance their capabilities. Evaluation platforms like AgentBench, WebArena, and ToolLLM provide robust methods for assessing these agents in complex scenarios. These advancements are leading to the development of more resilient and capable autonomous agents, anticipated to become integral in our digital lives, assisting in tasks from email responses to disease diagnosis. The future of AI, with LLMs at the forefront, is promising.

Exploring Autonomous Agents through the Lens of Large Language Models: A Review

TL;DR

This review examines the integration of Large Language Models (LLMs) into autonomous agents, detailing transformer foundations, memory/planning/action architectures, and diverse prompting strategies. It surveys how tools and grounding (RAG, APIs) mitigate limitations like hallucinations and enable real-world task execution, supported by evaluation platforms such as AgentBench, WebArena, and ToolLLM. The paper highlights current performance gaps, implementation constraints, and methods to improve alignment, multimodality, and agent ecosystems, offering a roadmap toward robust, real-world autonomous agents. Collectively, the work underscores the potential of LLM-driven agents across domains while acknowledging challenges that require continued research and practical evaluation frameworks.

Abstract

Large Language Models (LLMs) are transforming artificial intelligence, enabling autonomous agents to perform diverse tasks across various domains. These agents, proficient in human-like text comprehension and generation, have the potential to revolutionize sectors from customer service to healthcare. However, they face challenges such as multimodality, human value alignment, hallucinations, and evaluation. Techniques like prompting, reasoning, tool utilization, and in-context learning are being explored to enhance their capabilities. Evaluation platforms like AgentBench, WebArena, and ToolLLM provide robust methods for assessing these agents in complex scenarios. These advancements are leading to the development of more resilient and capable autonomous agents, anticipated to become integral in our digital lives, assisting in tasks from email responses to disease diagnosis. The future of AI, with LLMs at the forefront, is promising.
Paper Structure (29 sections, 5 figures)

This paper contains 29 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Background on Large Language Models and LLM-based Autonomous Agents
  3. Transformer Architecture
  4. The Evolution of Large Language Models
  5. Building Autonomous Agents with Large Language Models
  6. All About Memory
  7. From LLM with Autonomous Agents
  8. The Art of Reasoning and Acting
  9. Prompting is all you need
  10. Evaluating Autonomous Agents
  11. Traditional Evaluation Frameworks
  12. AgentBench: An All-Encompassing Evaluation Framework
  13. LLMs Performance in AgentBench
  14. Enhancing Agent Performance
  15. Comparison with Traditional Evaluation Frameworks
  16. ...and 14 more sections

Figures (5)

  • Figure 1: Architecture of Transformer (Based on vaswani2023attention)
  • Figure 2: The Procedures of RLHF and DPO
  • Figure 3: Overview of Retrieval Augmented Generation
  • Figure 4: AlphaCode's Approach to Code Generation
  • Figure 5: MedFuseNet's Approach to Visual Question Answering