Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
38 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey (2404.11584v1)

Published 17 Apr 2024 in cs.AI and cs.CL
The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey

Abstract: This survey paper examines the recent advancements in AI agent implementations, with a focus on their ability to achieve complex goals that require enhanced reasoning, planning, and tool execution capabilities. The primary objectives of this work are to a) communicate the current capabilities and limitations of existing AI agent implementations, b) share insights gained from our observations of these systems in action, and c) suggest important considerations for future developments in AI agent design. We achieve this by providing overviews of single-agent and multi-agent architectures, identifying key patterns and divergences in design choices, and evaluating their overall impact on accomplishing a provided goal. Our contribution outlines key themes when selecting an agentic architecture, the impact of leadership on agent systems, agent communication styles, and key phases for planning, execution, and reflection that enable robust AI agent systems.

PDF HTML Abstract

Overview of Recent AI Agent Implementations and Architectures

Introduction

Recent advancements in AI agent implementations have notably expanded the functionality of AI systems, particularly in enhancing reasoning, planning, and tool execution capabilities. This paper reviews both single-agent and multi-agent architectures, discussing their design choices, capabilities, and impact on achieving complex objectives.

Single-Agent vs Multi-Agent Systems

The research highlights core differences between single-agent and multi-agent systems, their suitability for various tasks, and the influence of architectural choices on goal accomplishment.

Single-Agent Architectures

Single-agent architectures typically handle tasks independently where interaction with other agents or personalities is unnecessary. These systems excel in environments where tasks are well-defined and feedback loops from external agents aren't crucial. Despite the simplicity and efficiency of single-agent systems in specific scenarios, they face challenges in adapting to new, undefined conditions or in escaping ineffective operational loops without external input.

Multi-Agent Architectures

In contrast, multi-agent architectures thrive in complex environments requiring collaborative effort and diverse input to accomplish tasks. Such systems benefit significantly from interactions among multiple agents, which collectively contribute to a more robust and adaptable solution. However, the complexity of coordinating between multiple agents introduces challenges such as maintaining efficient communication and preventing conflict or redundancy in tasks.

Impact of Design Choices

The paper discusses several critical design elements that influence the effectiveness of AI agents. These include the leaders' role within systems, methods of agent communication, and operational phases like planning, execution, and reflection.

Leadership and System Design

Leadership within multi-agent systems is crucial for coordinating efforts and maintaining a clear task structure among agents. Systems with a defined leadership hierarchy tend to perform better by reducing operational redundancy and focusing the group's efforts.

Communication Strategies

Agent communication styles—whether hierarchical or egalitarian—significantly affect the system’s operation. Vertical communication systems simplify command structures and reduce conflict potential, while horizontal systems may encourage innovation and adaptability by allowing free information flow among agents.

Operational Phases

The phases of planning, execution, and reflection are critical in all AI agent architectures. Robust systems tend to feature well-defined phases that guide the agents through task completion, allowing for adjustments based on feedback and reflective observations.

Future Directions in AI Agent Research

Looking forward, the paper suggests several avenues for enhancing AI agent architectures:

  1. Enhanced Agent Reasoning: Improving the reasoning capabilities of agents to handle more complex, multi-faceted problems can lead to broader applicability in real-world scenarios.
  2. Dynamic Agent Configurations: Systems that can adjust their agent configurations dynamically in response to task requirements or environmental changes could be more efficient and adaptable.
  3. Robust Multi-Agent Coordination: Developing more sophisticated methods for agent coordination and communication can prevent inefficiencies and improve outcomes in systems where multiple agents interact.

Conclusion

AI agent architectures have evolved significantly, presenting new opportunities to tackle complex and dynamic problems. Both single-agent and multi-agent systems have their merits and are preferable under different circumstances. The continued development and refinement of these systems will likely focus on enhancing adaptability, reasoning capabilities, and the efficiency of system-wide coordination. Further research in these areas will pave the way for more capable, versatile AI agents, potentially transforming how tasks are approached and executed across various domains.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. “AutoGPT+P: Affordance-based Task Planning with Large Language Models” arXiv:2402.10778 [cs] version: 1 arXiv, 2024 URL: http://arxiv.org/abs/2402.10778
  2. “AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors” arXiv:2308.10848 [cs] arXiv, 2023 URL: http://arxiv.org/abs/2308.10848
  3. “Training Verifiers to Solve Math Word Problems” arXiv:2110.14168 [cs] arXiv, 2021 URL: http://arxiv.org/abs/2110.14168
  4. “Large Language Model-based Human-Agent Collaboration for Complex Task Solving”, 2024 arXiv:2402.12914 [cs.CL]
  5. “Bias and Fairness in Large Language Models: A Survey” arXiv:2309.00770 [cs] arXiv, 2024 URL: http://arxiv.org/abs/2309.00770
  6. “Efficient Tool Use with Chain-of-Abstraction Reasoning” arXiv:2401.17464 [cs] arXiv, 2024 URL: http://arxiv.org/abs/2401.17464
  7. “Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies” arXiv:2101.02235 [cs] arXiv, 2021 URL: http://arxiv.org/abs/2101.02235
  8. “Time Travel in LLMs: Tracing Data Contamination in Large Language Models” arXiv:2308.08493 [cs] version: 3 arXiv, 2024 URL: http://arxiv.org/abs/2308.08493
  9. “Embodied LLM Agents Learn to Cooperate in Organized Teams”, 2024 arXiv:2403.12482 [cs.AI]
  10. “Measuring Massive Multitask Language Understanding” arXiv:2009.03300 [cs] arXiv, 2021 URL: http://arxiv.org/abs/2009.03300
  11. “MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework”, 2023 arXiv:2308.00352 [cs.AI]
  12. “Understanding the planning of LLM agents: A survey”, 2024 arXiv:2402.02716 [cs.AI]
  13. “SWE-bench: Can Language Models Resolve Real-World GitHub Issues?” arXiv:2310.06770 [cs] arXiv, 2023 URL: http://arxiv.org/abs/2310.06770
  14. “S3Eval: A Synthetic, Scalable, Systematic Evaluation Suite for Large Language Models” arXiv:2310.15147 [cs] arXiv, 2023 URL: http://arxiv.org/abs/2310.15147
  15. “Graph-enhanced Large Language Models in Asynchronous Plan Reasoning” arXiv:2402.02805 [cs] arXiv, 2024 URL: http://arxiv.org/abs/2402.02805
  16. “From LLM to Conversational Agent: A Memory Enhanced Architecture with Fine-Tuning of Large Language Models” arXiv:2401.02777 [cs] arXiv, 2024 URL: http://arxiv.org/abs/2401.02777
  17. “AgentBench: Evaluating LLMs as Agents” arXiv:2308.03688 [cs] arXiv, 2023 URL: http://arxiv.org/abs/2308.03688
  18. “Dynamic LLM-Agent Network: An LLM-agent Collaboration Framework with Agent Team Optimization”, 2023 arXiv:2310.02170 [cs.CL]
  19. Yohei Nakajima “yoheinakajima/babyagi” original-date: 2023-04-03T00:40:27Z, 2024 URL: https://github.com/yoheinakajima/babyagi
  20. “AI Deception: A Survey of Examples, Risks, and Potential Solutions” arXiv:2308.14752 [cs] arXiv, 2023 URL: http://arxiv.org/abs/2308.14752
  21. “Personality Traits in Large Language Models”, 2023 arXiv:2307.00184 [cs.CL]
  22. “Learning to Use Tools via Cooperative and Interactive Agents” arXiv:2403.03031 [cs] arXiv, 2024 URL: http://arxiv.org/abs/2403.03031
  23. “Reflexion: Language Agents with Verbal Reinforcement Learning” arXiv:2303.11366 [cs] arXiv, 2023 URL: http://arxiv.org/abs/2303.11366
  24. “Systematic Biases in LLM Simulations of Debates” arXiv:2402.04049 [cs] arXiv, 2024 URL: http://arxiv.org/abs/2402.04049
  25. “Evil Geniuses: Delving into the Safety of LLM-based Agents” arXiv:2311.11855 [cs] arXiv, 2024 URL: http://arxiv.org/abs/2311.11855
  26. “Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?” arXiv:2402.18272 [cs] arXiv, 2024 URL: http://arxiv.org/abs/2402.18272
  27. “Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation” arXiv:2402.11443 [cs] arXiv, 2024 URL: http://arxiv.org/abs/2402.11443
  28. “Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration”, 2024 arXiv:2307.05300 [cs.AI]
  29. “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models” arXiv:2201.11903 [cs] arXiv, 2023 URL: http://arxiv.org/abs/2201.11903
  30. “SmartPlay: A Benchmark for LLMs as Intelligent Agents” arXiv:2310.01557 [cs] arXiv, 2024 URL: http://arxiv.org/abs/2310.01557
  31. “The Rise and Potential of Large Language Model Based Agents: A Survey”, 2023 arXiv:2309.07864 [cs.AI]
  32. “ReAct: Synergizing Reasoning and Acting in Language Models” arXiv:2210.03629 [cs] arXiv, 2023 URL: http://arxiv.org/abs/2210.03629
  33. “Tree of Thoughts: Deliberate Problem Solving with Large Language Models” arXiv:2305.10601 [cs] arXiv, 2023 URL: http://arxiv.org/abs/2305.10601
  34. “How Language Model Hallucinations Can Snowball” arXiv:2305.13534 [cs] arXiv, 2023 URL: http://arxiv.org/abs/2305.13534
  35. “(InThe)WildChat: 570K ChatGPT Interaction Logs In The Wild” In The Twelfth International Conference on Learning Representations, 2024 URL: https://openreview.net/forum?id=Bl8u7ZRlbM
  36. “Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models” arXiv:2310.04406 [cs] arXiv, 2023 URL: http://arxiv.org/abs/2310.04406
  37. “DyVal 2: Dynamic Evaluation of Large Language Models by Meta Probing Agents” arXiv:2402.14865 [cs] arXiv, 2024 URL: http://arxiv.org/abs/2402.14865
  38. “DyVal: Dynamic Evaluation of Large Language Models for Reasoning Tasks” arXiv:2309.17167 [cs] arXiv, 2024 URL: http://arxiv.org/abs/2309.17167
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (4)
  1. Tula Masterman (2 papers)
  2. Sandi Besen (2 papers)
  3. Mason Sawtell (2 papers)
  4. Alex Chao (5 papers)
Citations (21)
View on Semantic Scholar
Youtube Logo Streamline Icon: https://streamlinehq.com

YouTube

HackerNews

  1. Survey Study on AI Agent Architectures (2024) (77 points, 16 comments)
Reddit Logo Streamline Icon: https://streamlinehq.com

Reddit

  1. The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey (26 points, 5 comments)