mlsanityData to 14 Oct 2026

A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model

arXiv 2408.02920 ↗First posted 6 Aug 2024cs.SE cs.AIcited by 4

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

The rapid advancement of AI technology has led to widespread applications of agent systems across various domains. However, the need for detailed architecture design poses significant challenges in designing and operating these systems. This paper introduces a taxonomy focused on the architectures of foundation-model-based agents, addressing critical aspects such as functional capabilities and non-functional qualities. We also discuss the operations involved in both design-time and run-time phases, providing a comprehensive view of architectural design and operational characteristics. By unifying and detailing these classifications, our taxonomy aims to improve the design of foundation-model-based agents. Additionally, the paper establishes a decision model that guides critical design and runtime decisions, offering a structured approach to enhance the development of foundation-model-based agents. Our contributions include providing a structured architecture design option and guiding the development process of foundation-model-based agents, thereby addressing current fragmentation in the field.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 92 of its 149 references that are in the map. First posted on arXiv 6 Aug 2024.

Builds on

92 of 149 references.

Closest ancestors · 24

Same direction, newest first

  1. 2024A Review of Prominent Paradigms for LLM-Based Agents: Tool Use (Including RAG), Planning, and Feedback Learning9 Jun
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  12. 2023Harms from Increasingly Agentic Algorithmic Systems20 Feb

Other references · 68

  1. 2017Attention Is All You Needcited by 60,361
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  5. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  6. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  7. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  8. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  9. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  10. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,568
  11. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  12. 2023SWE-bench: Can Language Models Resolve Real-World GitHub Issues?cited by 3,373
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  3. 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,653
  4. 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,242
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  7. 2022Inner Monologue: Embodied Reasoning through Planning with Language Modelscited by 1,081
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  9. 2023Reasoning with Language Model is Planning with World Modelcited by 861
  10. 2024Evaluating Very Long-Term Conversational Memory of LLM Agentscited by 830
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  13. 2023MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Actioncited by 517
  14. 2023LLM+P: Empowering Large Language Models with Optimal Planning Proficiencycited by 501
  15. 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 399
  16. 2023Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memorycited by 289
  17. 2024CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challengescited by 282
  18. 2024When Can LLMs Actually Correct Their Own Mistakes? A Critical Survey of Self-Correction of LLMscited by 240
  19. 2024Tool Learning with Large Language Models: A Surveycited by 223
  20. 2023Leveraging Pre-trained Large Language Models to Construct and Utilize World Models for Model-based Task Planningcited by 207
  21. 2024OS-Copilot: Towards Generalist Computer Agents with Self-Improvementcited by 205
  22. 2024MapCoder: Multi-Agent Code Generation for Competitive Problem Solvingcited by 177
  23. 2024Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMscited by 176
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  36. 2023Automating Customer Service using LangChain: Building custom open-source GPT Chatbot for organizationscited by 61
  37. 2023Prompting Is All You Need: Automated Android Bug Replay with Large Language Modelscited by 40
  38. 2023Open-Ended Instructable Embodied Agents with Memory-Augmented Large Language Modelscited by 37

50 most cited of 68 All 68 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 3.

4 papers.

  1. 2026AI Agent Systems: Architectures, Applications, and Evaluationcited by 13
  2. 2024A Layered Architecture for Developing and Enhancing Capabilities in Large Language Model-based Software Systemscited by 1
  3. 2026Foundation-Model-Based Agents in Industrial Automation: Purposes, Capabilities, and Open Challengescited by 1
  4. 2026Describing Agentic AI Systems with C4: Lessons from Industry Projectscited by 0

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