From Fragmentation to Systematic Design: Architecting LLM-Based Multi-Agent Systems

The integration of Large Language Models (LLMs) has substantially facilitated the development of autonomous multi agent systems (MAS). However, the contemporary landscape appears characterized by fragmentation arising from incremental prompt engineering and heterogeneous workflow designs. To mitigate this conceptual disparity, the present survey articulates a unified architectural framework intended to advance MAS research from experimental prototyping toward a systematic architectural science. Recent scholarship is organized along five interacting dimensions comprising Agent Constitution, Multi Agent Orchestration, Evolution, Applications, and Evaluation. Through the decomposition of systems into modular profiles, memory substrates, and interaction topologies, the analysis suggests a discernible transition from heuristic scripts toward executable specifications. Complementing this structural view, application paradigms are synthesized into deterministic Digital Workforces and explorative Digital Laboratories, a dichotomy that implies the necessity for distinct task--oriented and coordination--oriented evaluation methodologies. Looking forward, the study identifies adaptive orchestration, risk governance, and process level observability as likely frontiers for the establishment of dependable multi agent infrastructures.