Study Analyzes Architectural Patterns in AI Agent Infrastructure

A new empirical study examines architectural design decisions in the non-LLM infrastructure supporting AI agent systems. Researchers conducted a protocol-guided investigation of 70 publicly available agent-system projects, analyzing source code and technical materials to identify recurring patterns. The study addresses three core questions about design dimensions, their co-occurrences, and emerging architectural patterns. Five recurring design dimensions were identified: subagent architecture, context management, tool systems, safety mechanisms, and orchestration. The research contributes a transparent methodology for analyzing heterogeneous agent-system corpora through source-code examination. Findings indicate the corpus shows preference for file-persistent, hybrid approaches. The paper was announced on arXiv with identifier 2604.18071v1, highlighting that architectural decisions in agent infrastructure remain understudied despite increasing reliance on reusable engineering components for tool mediation, context handling, delegation, safety control, and orchestration.