🤖 AI Summary
This study addresses the lack of systematic investigation into architectural design decisions for non-large language model components in current AI agent systems. The authors propose a protocol-guided, source code–driven empirical analysis method that enables, for the first time, transparent deconstruction of heterogeneous AI agent systems. Through cross-project qualitative coding and co-occurrence analysis of 70 open-source projects, they identify five core design dimensions—sub-agent architecture, context management, tooling systems, security mechanisms, and orchestration—and uncover their combinatorial patterns. Based on these findings, the study further distills five archetypal architectural patterns: lightweight tool-oriented, CLI framework–based, multi-agent orchestrator, enterprise system, and domain-specific vertical architectures.
📝 Abstract
AI agent systems increasingly rely on reusable non-LLM engineering infrastructure that packages tool mediation, context handling, delegation, safety control, and orchestration. Yet the architectural design decisions in this surrounding infrastructure remain understudied. This paper presents a protocol-guided, source-grounded empirical study of 70 publicly available agent-system projects, addressing three questions: which design-decision dimensions recur across projects, which co-occurrences structure those decisions, and which typical architectural patterns emerge. Methodologically, we contribute a transparent investigation procedure for analyzing heterogeneous agent-system corpora through source-code and technical-material reading. Empirically, we identify five recurring design dimensions (subagent architecture, context management, tool systems, safety mechanisms, and orchestration) and find that the corpus favors file-persistent, hybrid, and hierarchical context strategies; registry-oriented tool systems remain dominant while MCP- and plugin-oriented extensions are emerging; and intermediate isolation is common but high-assurance audit is rare. Cross-project co-occurrence analysis reveals that deeper coordination pairs with more explicit context services, stronger execution environments with more structured governance, and formalized tool-registration boundaries with broader ecosystem ambitions. We synthesize five recurring architectural patterns spanning lightweight tools, balanced CLI frameworks, multi-agent orchestrators, enterprise systems, and scenario-verticalized projects. The result provides an evidence-based account of architectural regularities in agent-system engineering, with grounded guidance for framework designers, selectors, and researchers.