🏛 Feed in a requirements spec and get 4+1-view architecture diagrams, production-ready docs, and an ATAM-style evaluation report, all automatically. Four specialized agents bridge requirements and design. Title: Bridging Requirements and Architecture: Multi-Agent Orchestration with External Knowledge and Hierarchical Memory URL: 📝 Overview MAAD orchestrates the path from a software requirements spec (SRS) to architecture design with four role-specialized agents. External knowledge via RAG and a three-layer hierarchical memory keep the result consistent and traceable. ❓ Challenges Solved Architecture design is complex and knowledge-intensive, so it relied heavily on architects. Single LLMs produce inconsistent output with incomplete requirement coverage, and existing multi-agent systems lacked architecture-specific workflows and knowledge integration. 💡 Methodology & Proposed Approach ・An Analyst extracts requirements (FR/NFR/ASR), a Modeler turns them into 4+1-view UML diagrams, and a Designer produces production-ready docs ・An Evaluator adds quality gates at each stage with traceability checks and ATAM-based analysis ・It embeds standards like ISO/IEC/IEEE 42010 and canonical textbooks into a vector DB, retrieving the top 3 per query ・A three-layer memory (working, episodic, semantic) supports iterative refinement and knowledge reuse 🎯 Use Cases It fits rapid architecture design from requirements, keeping design consistent as requirements evolve, knowledge transfer that doesn't rely on tacit expertise, and reducing review effort through automated validation. 📊 Experimental Results ・On 10 real-world SRS cases, MAAD generates more complete, modular, and traceable architectures than MetaGPT ・It's evaluated on seven architecture metrics like coupling and cohesion, with the Evaluator auto-producing quality reports ・Among the LLMs, GPT-5.2 and Qwen3.5 outperformed others across most settings ・Six practicing architects judged the designs principle-aligned and suitable for real development #SoftwareArchitecture# #AIAgents#