Architecture Exploration and Reflection Meet LLM-based Agents

The exploration of architecture alternatives is an essential part of the architecture design process, in which designers search and assess solutions for their requirements. Although automated tools and techniques have been proposed for this process, they still face adoption challenges. Nowadays, the emergence of generative AI techniques creates an opportunity for leveraging natural language representations in architecture design, particularly through LLM-based agents. To date, these agents have been mostly focused on coding-related tasks or requirements analysis. In this work, we investigate an approach for defining design agents, which can autonomously search for architectural patterns and tactics for a particular system and requirements using a textual format. In addition to incorporating architectural knowledge, these agents can reflect on the pros and cons of the proposed decisions, enabling a feedback loop towards improving the decisions' quality. We present a proof-of-concept called ReArch that adapts elements from the ReAct and LATS agent frameworks, and discuss initial results of applying our LLM-based agents to a case study considering different patterns.