# MAAD: Automate Software Architecture Design through Knowledge-Driven Multi-Agent Collaboration

arXiv: https://arxiv.org/abs/2507.21382 · HTML: https://mlsanity.com/paper/2507.21382/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 20 of its 34 references that are in the map.
- First posted on arXiv 28 Jul 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 20 of 20)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://mlsanity.com/paper/2309.01219/) (2023)
- [Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents](https://mlsanity.com/paper/2306.03314/) (2023)
- [LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead](https://mlsanity.com/paper/2404.04834/) (2024)
- [Towards Human-Bot Collaborative Software Architecting with ChatGPT](https://mlsanity.com/paper/2302.14600/) (2023)
- [MARE: Multi-Agents Collaboration Framework for Requirements Engineering](https://mlsanity.com/paper/2405.03256/) (2024)
- [Can LLMs Generate Architectural Design Decisions? -An Exploratory Empirical study](https://mlsanity.com/paper/2403.01709/) (2024)
- [Towards Detecting LLMs Hallucination via Markov Chain-based Multi-agent Debate Framework](https://mlsanity.com/paper/2406.03075/) (2024)
- [Generative AI for Software Architecture. Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2503.13310/) (2025)
- [Software Architecture Meets LLMs: A Systematic Literature Review](https://mlsanity.com/paper/2505.16697/) (2025)
- [From Requirements to Architecture: An AI-Based Journey to Semi-Automatically Generate Software Architectures](https://mlsanity.com/paper/2401.14079/) (2024)
- [An Evaluation of Requirements Modeling for Cyber-Physical Systems via LLMs](https://mlsanity.com/paper/2408.02450/) (2024)
- [Knowledge-Based Multi-Agent Framework for Automated Software Architecture Design](https://mlsanity.com/paper/2503.20536/) (2025)
- [Software Architecture in Practice: Challenges and Opportunities](https://mlsanity.com/paper/2308.09978/) (2023)
- [Unveiling the Role of ChatGPT in Software Development: Insights from Developer-ChatGPT Interactions on GitHub](https://mlsanity.com/paper/2505.03901/) (2025)

## Built on by (most cited first, 1 of 1)

- [Automated Summarization of Software Documents: An LLM-based Multi-Agent Approach](https://mlsanity.com/paper/2606.24689/) (2026)

## Abstract (arXiv)

Software architecture design is a critical, yet inherently complex and knowledge-intensive phase of software development. It requires deep domain expertise, development experience, architectural knowledge, careful trade-offs among competing quality attributes, and the ability to adapt to evolving requirements. Traditionally, this process is time-consuming and labor-intensive, and relies heavily on architects, often resulting in limited design alternatives, especially under the pressures of agile development. While Large Language Model (LLM)-based agents have shown promising performance across various SE tasks, their application to architecture design remains relatively scarce and requires more exploration, particularly in light of diverse domain knowledge and complex decision-making. To address the challenges, we proposed MAAD (Multi-Agent Architecture Design), an automated framework that employs a knowledge-driven Multi-Agent System (MAS) for architecture design. MAAD orchestrates four specialized agents (i.e., Analyst, Modeler, Designer and Evaluator) to collaboratively interpret requirements specifications and produce architectural blueprints enriched with quality attributes-based evaluation reports. We then evaluated MAAD through a case study and comparative experiments against MetaGPT, a state-of-the-art MAS baseline. Our results show that MAAD's superiority lies in generating comprehensive architectural components and delivering insightful and structured architecture evaluation reports. Feedback from industrial architects across 11 requirements specifications further reinforces MAAD's practical usability. We finally explored the performance of the MAAD framework with three LLMs (GPT-4o, DeepSeek-R1, and Llama 3.3) and found that GPT-4o exhibits better performance in producing architecture design, emphasizing the importance of LLM selection in MAS-driven architecture design.