# The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey

**Authors:** Tula Masterman, Sandi Besen, Mason Sawtell, Alex Chao  
**arXiv:** [2404.11584](https://arxiv.org/abs/2404.11584)  
**Date:** 2024-04-17  
**Summary page:** https://www.summarizepaper.com/en/paper/2404.11584v1/

## Key points

- The survey paper explores emerging AI agent architectures for reasoning, planning, and tool calling.
- Recent advancements in AI agent implementations aim to enhance reasoning, planning, and tool execution capabilities to achieve complex goals.
- The paper discusses current capabilities and limitations of existing AI agents, shares insights from observations of these systems in action, and suggests important considerations for future developments in their design.
- Overviews of both single-agent and multi-agent architectures are provided, identifying key patterns and divergences in design choices and evaluating their impact on accomplishing goals.
- Key themes when selecting an agentic architecture are outlined, including the impact of leadership on agent systems, communication styles between agents, and key phases for planning, execution, and reflection that enable robust AI agent systems.
- There is a debate within the research community regarding whether single or multi-agent systems are better suited for solving complex tasks. Single-agent architectures excel in well-defined problems while multi-agent architectures thrive in scenarios requiring collaboration.
- Employing a "criticize-reflect" step for generating plans, evaluating performance, providing feedback, and reorganizing teams is emphasized.
- Agents with dynamic team structures featuring rotating leadership deliver superior performance with lower time to task completion and communication costs on average. Leadership and dynamic team structures enhance the overall team's ability to reason effectively plan efficiently.

## Summary

This survey paper explores emerging AI agent architectures for reasoning, planning, and tool calling. It focuses on recent advancements in AI agent implementations that aim to achieve complex goals by enhancing reasoning, planning, and tool execution capabilities. The primary objectives are to communicate current capabilities and limitations of existing AI agents, share insights from observations of these systems in action, and suggest important considerations for future developments in their design. The authors provide overviews of both single-agent and multi-agent architectures, identifying key patterns and divergences in design choices and evaluating their overall impact on accomplishing a given goal. Key themes when selecting an agentic architecture are outlined, along with discussions on the impact of leadership on agent systems, communication styles between agents, and key phases for planning, execution, and reflection that enable robust AI agent systems. The paper delves into the debate within the research community regarding whether single or multi-agent systems are better suited for solving complex tasks. While single-agent architectures excel in well-defined problems where feedback from other agents or users is not necessary, multi-agent architectures thrive in scenarios requiring collaboration and multiple distinct execution paths. Furthermore,<DateTime> importance of employing a "criticize-reflect" step for generating plans,<DateTime> evaluating performance,<DateTime> providing feedback,<DateTime> and re-organizing teams is emphasized. Results indicate that agents with dynamic team structures featuring rotating leadership deliver superior performance with lower time to task completion<DateTime> communication costs on average.<DateTime> Ultimately,<DateTime> leadership<DateTime> dynamic team structures enhance the overall team's ability to reason,<DateTime> plan effectively<DateTime>, <DateTime>and perform tasks efficiently.