Does Architecture Matter? Comparing Multi-agent Workflows for Filtering and Prioritizing Static Analysis Findings

Abstract

The number of exploited software vulnerabilities rises every year, and their impact grows as software systems become ever more interconnected. To detect such vulnerabilities in large codebases, many organizations rely on Static Application Security Testing (SAST) tools. However, SAST tools can produce high volumes of false positives, which increases manual triage effort, causes alert fatigue, and can lead developers to dismiss warnings that contain real vulnerabilities. Large Language Models (LLMs) and AI agents are increasingly used to filter such findings, and it is already well established that newer, stronger models yield better results. Less is known about an orthogonal design choice: does the multi-agent workflow architecture itself change filtering performance? We implement a modular multi-agent framework comprising six agents and four distinct workflow architectures: single-agent, sequential pipeline, parallel debate, and deep agent. We evaluate the architectures on two synthetic open-source benchmarks and one real-world benchmark containing actual Common Vulnerabilities and Exposures (CVEs) in full repositories, using three different models. The evaluation results show that, for a fixed model, the choice of architecture notably shifts filtering performance, with the Multi-Agent Debate architecture achieving the strongest filtering, albeit also at the highest token cost. Moreover, the workflows' design decisions, such as passing prior reasoning between agents or looping findings back for re-auditing, govern a controllable trade-off between false-positive reduction and recall. We further show that tool-aware retrieval improves recall without harming precision, and that agent-generated context makes vulnerability prioritization markedly more stable and steerable.