Agents as Architecture Evaluators: Automating ATAM and SAAM for Continuous Assessment

Abstract

The long-term success of a software system relies heavily on its un-derlying architecture, which dictates its performance with respect to quality attributes. Software Architecture Evaluation methods, such as the Architecture Tradeoff Analysis Method (ATAM) and the Software Architecture Analysis Method (SAAM), are widely recognized for their ability to mitigate risks and ensure reliable quality attributes. Their adoption remains limited due to cost, time, and expertise. We investigate agentic AI to lower these barriers. From human-conducted evaluations in the literature, we built a dataset of 39 cases and an agentic workflow that synthesizes evaluation reports from high-level architectural descriptions. Automated metrics and human evaluation yield relevance and completeness of 4.5/5 and 3.9/5. Agents automated scenario generation and risk identification, under 5 minutes at less than $0.50 per run.