Bridging Requirements and Architecture: Multi-Agent Orchestration with External Knowledge and Hierarchical Memory

Researchers propose MAAD (Multi-Agent Architecture Design), a framework using orchestrated AI agents with external knowledge and hierarchical memory to automate software architecture design from requirements. The system outperforms existing approaches and demonstrates that advanced LLMs significantly improve architectural quality and validation efficiency.

MAAD addresses a fundamental challenge in software engineering: transforming business requirements into robust, scalable architectural designs. Traditionally, this process demands extensive expertise and time, limiting exploration of alternative solutions under agile development constraints. The framework's innovation lies in its multi-agent orchestration strategy, where specialized agents handle distinct phases—analysis, modeling, design, and evaluation—while maintaining contextual awareness through hierarchical memory systems.

The architecture design problem has intensified as systems grow more complex and organizations demand faster delivery cycles. Previous AI approaches to software engineering have shown promise in coding and testing, but architecture design remained relatively untouched due to its requirement for nuanced trade-off analysis and domain knowledge. MAAD tackles this by integrating Retrieval-Augmented Generation (RAG) to inject industry standards and established patterns, effectively augmenting agent reasoning with collective architectural wisdom.

The experimental results carry practical implications for development teams. By automating architectural validation and generating structured quality assessments, MAAD reduces manual review burden while improving design completeness and modularity. The finding that model reasoning capacity directly correlates with output quality suggests that as LLMs advance, downstream engineering applications will unlock proportional productivity gains.

Future development hinges on extending MAAD's framework to handle increasingly complex distributed systems and real-time architectural evolution. The success metrics demonstrate measurable improvements over MetaGPT, establishing a new baseline for AI-assisted architecture design. As organizations adopt such tools, we should expect architectural decisions to become more data-driven and less dependent on individual expert judgment.

• →Multi-agent orchestration with specialized roles outperforms single-model approaches in software architecture generation.
• →RAG integration enables AI agents to leverage established architectural standards and patterns effectively.
• →Advanced LLMs (GPT-5.2, Qwen3.5) show significantly better reasoning capacity for complex architectural trade-offs.
• →Automated quality evaluation reduces manual validation efforts while improving architectural consistency and traceability.
• →Hierarchical memory mechanisms capture design history, enabling iterative refinement and context-aware decision making.