This survey examines the recent advancements in AI agent implementations, focusing on single-agent and multi-agent architectures and their capabilities in reasoning, planning, and tool execution. The paper provides detailed insights into:

• Single-Agent vs Multi-Agent Systems: Comparison and implications for diverse scenarios.
• Key Themes in Agent Architecture: How leadership, communication, and planning phases influence success.
• Evaluation of Systems: Based on goal accomplishment.

The significance of this study lies in its comprehensive review of varied AI agent architectures, critically evaluating their execution strategies and suggesting modifications for future enhancements. It lays a foundation for understanding how these systems can be better tailored to meet specific operational needs, even proposing further research into adaptive and more robust agent frameworks.