Exploring AI Agents Enhanced by LLMs

The surge in capabilities of large language models (LLMs) has driven a wave of innovative applications in AI agents. This paper presents an extensive comparison between LLM-based agents and traditional AI systems, focusing on attributes like natural language handling, knowledge storage, and reasoning.

Highlights from the survey:

• Memory and Planning: Innovative approaches to memory and planning in AI agents.
• Tool Use: Examination of how LLMs enhance tool use in agents for better performance.
• Future Research Directions: Suggestions for areas that need further exploration to enhance the absorption of LLM benefits into AI architectures.

Why This Survey Matters The paper’s thorough analysis provides foundational knowledge that can inspire future advancements in AI agent design. It offers deep insights into the integration of complex language models with traditional computer science approaches to create more capable and efficient systems.